About | BLOGS | Portfolio | Misc | Recommended | What's New | What's Hot

About | BLOGS | Portfolio | Misc | Recommended | What's New | What's Hot


Bibliography Options Menu

30 Jun 2022 at 01:35
Hide Abstracts   |   Hide Additional Links
Long bibliographies are displayed in blocks of 100 citations at a time. At the end of each block there is an option to load the next block.

Bibliography on: Brain-Computer Interface


Robert J. Robbins is a biologist, an educator, a science administrator, a publisher, an information technologist, and an IT leader and manager who specializes in advancing biomedical knowledge and supporting education through the application of information technology. More About:  RJR | OUR TEAM | OUR SERVICES | THIS WEBSITE

RJR: Recommended Bibliography 30 Jun 2022 at 01:35 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: bci OR (brain-computer interface) OR (brain-machine interface) OR (mind-machine interface) OR (neural-control interface) NOT 26799652[PMID] NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)


RevDate: 2022-06-29

Sun G, Zeng F, McCartin M, et al (2022)

Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents.

Science translational medicine, 14(651):eabm5868.

Effective treatments for chronic pain remain limited. Conceptually, a closed-loop neural interface combining sensory signal detection with therapeutic delivery could produce timely and effective pain relief. Such systems are challenging to develop because of difficulties in accurate pain detection and ultrafast analgesic delivery. Pain has sensory and affective components, encoded in large part by neural activities in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC), respectively. Meanwhile, studies show that stimulation of the prefrontal cortex (PFC) produces descending pain control. Here, we designed and tested a brain-machine interface (BMI) combining an automated pain detection arm, based on simultaneously recorded local field potential (LFP) signals from the S1 and ACC, with a treatment arm, based on optogenetic activation or electrical deep brain stimulation (DBS) of the PFC in freely behaving rats. Our multiregion neural interface accurately detected and treated acute evoked pain and chronic pain. This neural interface is activated rapidly, and its efficacy remained stable over time. Given the clinical feasibility of LFP recordings and DBS, our findings suggest that BMI is a promising approach for pain treatment.

RevDate: 2022-06-29

Yao L, Jiang N, Mrachacz-Kersting N, et al (2022)

Reducing the Calibration Time in Somatosensory BCI by using tactile ERD.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

OBJECTIVE: We propose a tactile-induced-oscillation approach to reduce the calibration time in somatosensory brain-computer interfaces (BCI).

METHODS: Based on the similarity between tactile induced event-related desynchronization (ERD) and imagined sensation induced ERD activation, we extensively evaluated BCI performance when using a conventional and a novel calibration strategy. In the conventional calibration, the tactile imagined data was used, while in the sensory calibration model sensory stimulation data was used. Subjects were required to sense the tactile stimulus when real tactile was applied to the left or right wrist and were required to perform imagined sensation tasks in the somatosensory BCI paradigm.

RESULTS: The sensory calibration led to a significantly better performance than the conventional calibration when tested on the same imagined sensation dataset (F(1,19)=10.89, P=0.0038), with an average 5.1% improvement in accuracy. Moreover, the sensory calibration was 39.3% faster in reaching a performance level of above 70% accuracy.

CONCLUSION: The proposed approach of using tactile ERD from the sensory cortex provides an effective way of reducing the calibration time in a somatosensory BCI system.

SIGNIFICANCE: The tactile stimulation would be specifically useful before BCI usage, avoiding excessive fatigue when the mental task is difficult to perform. The tactile ERD approach may find BCI applications for patients or users with preserved afferent pathways.

RevDate: 2022-06-29

Fei W, Bi L, Wang J, et al (2022)

Effects of Cognitive Distraction on Upper Limb Movement Decoding from EEG Signals.

IEEE transactions on bio-medical engineering, PP: [Epub ahead of print].

OBJECTIVE: Hand movement decoding from electroencephalograms (EEG) signals is vital to the rehabilitation and assistance of upper limb-impaired patients. Few existing studies on hand movement decoding from EEG signals consider any distractions. However, in practice, patients can be distracted while using the hand movement decoding systems in real life. In this paper, we aim to investigate the effects of cognitive distraction on movement decoding performance.

METHODS: We first propose a robust decoding method of hand movement directions to cognitive distraction from EEG signals by using the Riemannian Manifold to extract affine invariant features and Gaussian Naive Bayes classifier (named RM-GNBC). Then, we use the experimental and simulated EEG data under conditions without and with distraction to compare the decoding performance of three decoding methods (including the proposed method, tangent space linear discriminant analysis (TSLDA), and baseline method)).

RESULTS: The simulation and experimental results show that the Riemannian-based methods (i.e., RM-GNBC and TSLDA) have higher accuracy under the conditions without and with cognitive distraction and smaller decreases in decoding accuracy between the conditions without and with cognitive distraction than the baseline method. Furthermore, the RM-GNBC method has 6% (paired t-test, p = 0.026) and 5% (paired t-test, p = 0.137) higher accuracies than the TSLDA method under the conditions without and with cognitive distraction, respectively.

CONCLUSION: The results show that the Riemannian-based methods have higher robustness to cognitive distraction.

SIGNIFICANCE: This work contributes to developing a brain-computer interface (BCI) to improve the rehabilitation and assistance of hand-impaired patients in real life and open an avenue to the studies on the effects of distraction on other BCI paradigms.

RevDate: 2022-06-27

Qin Y, Yang B, Li H, et al (2022)

Immobilized BiOCl0.75I0.25/g-C3N4 nanocomposites for photocatalytic degradation of bisphenol A in the presence of effluent organic matter.

The Science of the total environment pii:S0048-9697(22)03925-0 [Epub ahead of print].

The BiOCl0.75I0.25/g-C3N4 nanosheet (BCI-CN) was successfully immobilized on polyolefin polyester fiber (PPF) through the hydrothermal method. The novel immobilized BiOCl0.75I0.25/g-C3N4 nanocomposites (BCI-CN-PPF) were characterized by scanning electron microscope (SEM), energy dispersive spectroscopy EDS, X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and UV-vis diffuse reflectance spectroscopy (UV-vis DRS) to confirm that BCI-CN was successfully immobilized on PPF with abundant oxygen vacancies reserved. Under simulated solar light irradiation, 100 % of bisphenol A (BPA) with an initial concentration of 10 mg·L-1 was degraded by BCI-CN-PPF (0.2 g·L-1 of BCI-CN immobilized) after 60 min. A similar photocatalytic efficiency of BPA was obtained in the presence of effluent organic matter (EfOM). The photocatalytic degradation of BPA was not affected by EfOM <5 mg-C/L. In comparison, the photocatalytic performance was considerably inhibited by EfOM with a concentration of 10 mg-C/L. Furthermore, photogenerated holes and superoxide radicals predominated in the photocatalytic degradation processes of BPA. The total organic carbon (TOC) removal efficiencies of BPA and EfOM were 75.2 % and 50 % in the BCI-CN-PPF catalytic system. The BPA removal efficiency of 94.9 % was still achieved in the eighth cycle of repeated use. This study provides a promising immobilized nanocomposite with high photocatalytic activity and excellent recyclability and reusability for practical application in wastewater treatment.

RevDate: 2022-06-27

Mirfathollahi A, Ghodrati MT, Shalchyan V, et al (2022)

Decoding locomotion speed and slope from local field potentials of rat motor cortex.

Computer methods and programs in biomedicine, 223:106961 pii:S0169-2607(22)00343-1 [Epub ahead of print].

BACKGROUND AND OBJECTIVE: Local Field Potentials (LFPs) recorded from the primary motor cortex (M1) have been shown to be very informative for decoding movement parameters, and these signals can be used to decode forelimb kinematic and kinetic parameters accurately. Although locomotion is one of the most basic and important motor abilities of humans and animals, the potential of LFPs in decoding abstract hindlimb locomotor parameters has not been investigated. This study investigates the feasibility of decoding speed and slope of locomotion, as two important abstract parameters of walking, using the LFP signals.

METHODS: Rats were trained to walk smoothly on a treadmill with different speeds and slopes. The brain signals were recorded using the microwire arrays chronically implanted in the hindlimb area of M1 while rats walked on the treadmill. LFP channels were spatially filtered using optimal common spatial patterns to increase the discriminability of speeds and slopes of locomotion. Logarithmic wavelet band powers were extracted as basic features, and the best features were selected using the statistical dependency criterion before classification.

RESULTS: Using 5 s LFP trials, the average classification accuracies of four different speeds and seven different slopes reached 90.8% and 86.82%, respectively. The high-frequency LFP band (250-500 Hz) was the most informative band about these parameters and contributed more than other frequency bands in the final decoder model.

CONCLUSIONS: Our results show that the LFP signals in M1 accurately decode locomotion speed and slope, which can be considered as abstract walking parameters needed for designing long-term brain-computer interfaces for hindlimb locomotion control.

RevDate: 2022-06-27

Perez-Velasco S, Santamaria-Vazquez E, Martinez-Cagigal V, et al (2022)

EEGSym: Overcoming Inter-subject Variability in Motor Imagery Based BCIs with Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

In this study, we present a new Deep Learning (DL) architecture for Motor Imagery (MI) based Brain Computer Interfaces (BCIs) called EEGSym. Our implementation aims to improve previous state-of-the-art performances on MI classification by overcoming inter-subject variability and reducing BCI inefficiency, which has been estimated to affect 10-50% of the population. This convolutional neural network includes the use of inception modules, residual connections and a design that introduces the symmetry of the brain through the mid-sagittal plane into the network architecture. It is complemented with a data augmentation technique that improves the generalization of the model and with the use of transfer learning across different datasets. We compare EEGSym's performance on inter-subject MI classification with ShallowConvNet, DeepConvNet, EEGNet and EEG-Inception. This comparison is performed on 5 publicly available datasets that include left or right hand motor imagery of 280 subjects. This population is the largest that has been evaluated in similar studies to date. EEGSym significantly outperforms the baseline models reaching accuracies of 88.6±9.0 on Physionet, 83.3±9.3 on OpenBMI, 85.1±9.5 on Kaya2018, 87.4±8.0 on Meng2019 and 90.2±6.5 on Stieger2021. At the same time, it allows 95.7% of the tested population (268 out of 280 users) to reach BCI control (≥70% accuracy). Furthermore, these results are achieved using only 16 electrodes of the more than 60 available on some datasets. Our implementation of EEGSym, which includes new advances for EEG processing with DL, outperforms previous state-of-the-art approaches on inter-subject MI classification.

RevDate: 2022-06-27

Semprini M, Arnulfo G, Delis I, et al (2022)

Editorial: Improving Neuroprosthetics Through Novel Techniques for Processing Electrophysiological Human Brain Signals.

Frontiers in neuroscience, 16:937801.

RevDate: 2022-06-27

Puttanawarut C, Sirirutbunkajorn N, Tawong N, et al (2022)

Impact of Interfractional Error on Dosiomic Features.

Frontiers in oncology, 12:726896.

Objectives: The purpose of this study was to investigate the stability of dosiomic features under random interfractional error. We investigated the differences in the values of features with different fractions and the error in the values of dosiomic features under interfractional error.

Material and Methods: The isocenters of the treatment plans of 15 lung cancer patients were translated by a maximum of ±3 mm in each axis with a mean of (0, 0, 0) and a standard deviation of (1.2, 1.2, 1.2) mm in the x, y, and z directions for each fraction. A total of 81 dose distributions for each patient were then calculated considering four fraction number groups (2, 10, 20, and 30). A total of 93 dosiomic features were extracted from each dose distribution in four different regions of interest (ROIs): gross tumor volume (GTV), planning target volume (PTV), heart, and both lungs. The stability of dosiomic features was analyzed for each fraction number group by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The agreements in the means of dosiomic features among the four fraction number groups were tested by ICC. The percent differences (PD) between the dosiomic features extracted from the original dose distribution and the dosiomic features extracted from the dose distribution with interfractional error were calculated.

Results: Eleven out of 93 dosiomic features demonstrated a large CV (CV ≥ 20%). Overall CV values were highest in GTV ROIs and lowest in lung ROIs. The stability of dosiomic features decreased as the total number of fractions decreased. The ICC results showed that five out of 93 dosiomic features had an ICC lower than 0.75, which indicates intermediate or poor stability under interfractional error. The mean dosiomic feature values were shown to be consistent with different numbers of fractions (ICC ≥ 0.9). Some of the dosiomic features had PD greater than 50% and showed different PD values with different numbers of fractions.

Conclusion: Some dosiomic features have low stability under interfractional error. The stability and values of the dosiomic features were affected by the total number of fractions. The effect of interfractional error on dosiomic features should be considered in further studies regarding dosiomics for reproducible results.

RevDate: 2022-06-27

Sladky V, Nejedly P, Mivalt F, et al (2022)

Distributed brain co-processor for tracking spikes, seizures and behaviour during electrical brain stimulation.

Brain communications, 4(3):fcac115 pii:fcac115.

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioural inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behaviour and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a handheld device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from nine humans and eight canines with epilepsy, and then implemented prospectively in out-of-sample testing in two pet canines and four humans with drug-resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain-stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures and correlation with patient behavioural reports. In the future, correlation of spikes and seizures with behaviour will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

RevDate: 2022-06-27

Galvin-McLaughlin D, Klee D, Memmott T, et al (2022)

Methodology and preliminary data on feasibility of a neurofeedback protocol to improve visual attention to letters in mild Alzheimer's disease.

Contemporary clinical trials communications, 28:100950 pii:S2451-8654(22)00067-9.

Background: Brain-computer interface (BCI) systems are controlled by users through neurophysiological input for a variety of applications, including communication, environmental control, and motor rehabilitation. Although individuals with severe speech and physical impairment are the primary users of this technology, BCIs have emerged as a potential tool for broader populations, including delivering cognitive training/interventions with neurofeedback (NFB).

Methods: This paper describes the development and preliminary testing of a protocol for use of a BCI system with NFB as an intervention for people with mild Alzheimer's disease (AD). The intervention focused on training visual attention and language skills, as AD is often associated with functional impairments in both. This funded pilot study called for enrolling five participants with mild AD in a six-week BCI EEG-based NFB intervention that followed a four-to-seven-week baseline phase. While two participants completed the study, the remaining three participants could not complete the intervention phase because of COVID-19 restrictions.

Results: Preliminary pilot results suggested: (1) participants with mild AD were able to participate in a study with multiple assessments per week and complete all outcome measures, (2) most outcome measures were reliable during the baseline phase, and (3) all participants with mild AD learned to operate a BCI spelling system with training.

Conclusions: Although preliminary results demonstrate practical feasibility to deliver NFB intervention using a BCI to adults with AD, completion of the protocol in its entirety with more participants is needed to further assess whether implementing NFB-based cognitive intervention is justified by functional treatment outcomes.

Trial registration: This study was registered with ClinicalTrials.gov (NCT03790774).

RevDate: 2022-06-27

Zhao X, Jin J, Xu R, et al (2022)

A Regional Smoothing Block Sparse Bayesian Learning Method With Temporal Correlation for Channel Selection in P300 Speller.

Frontiers in human neuroscience, 16:875851.

The P300-based brain-computer interfaces (BCIs) enable participants to communicate by decoding the electroencephalography (EEG) signal. Different regions of the brain correspond to various mental activities. Therefore, removing weak task-relevant and noisy channels through channel selection is necessary when decoding a specific type of activity from EEG. It can improve the recognition accuracy and reduce the training time of the subsequent models. This study proposes a novel block sparse Bayesian-based channel selection method for the P300 speller. In this method, we introduce block sparse Bayesian learning (BSBL) into the channel selection of P300 BCI for the first time and propose a regional smoothing BSBL (RSBSBL) by combining the spatial distribution properties of EEG. The RSBSBL can determine the number of channels adaptively. To ensure practicality, we design an automatic selection iteration strategy model to reduce the time cost caused by the inverse operation of the large-size matrix. We verified the proposed method on two public P300 datasets and on our collected datasets. The experimental results show that the proposed method can remove the inferior channels and work with the classifier to obtain high-classification accuracy. Hence, RSBSBL has tremendous potential for channel selection in P300 tasks.

RevDate: 2022-06-27

Sato Y, Schmitt O, Ip Z, et al (2022)

Pathological changes of brain oscillations following ischemic stroke.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism [Epub ahead of print].

Brain oscillations recorded in the extracellular space are among the most important aspects of neurophysiology data reflecting the activity and function of neurons in a population or a network. The signal strength and patterns of brain oscillations can be powerful biomarkers used for disease detection and prediction of the recovery of function. Electrophysiological signals can also serve as an index for many cutting-edge technologies aiming to interface between the nervous system and neuroprosthetic devices and to monitor the efficacy of boosting neural activity. In this review, we provided an overview of the basic knowledge regarding local field potential, electro- or magneto- encephalography signals, and their biological relevance, followed by a summary of the findings reported in various clinical and experimental stroke studies. We reviewed evidence of stroke-induced changes in hippocampal oscillations and disruption of communication between brain networks as potential mechanisms underlying post-stroke cognitive dysfunction. We also discussed the promise of brain stimulation in promoting post stroke functional recovery via restoring neural activity and enhancing brain plasticity.

RevDate: 2022-06-26

Wu D, Jiang X, R Peng (2022)

Transfer learning for motor imagery based brain-computer interfaces: A tutorial.

Neural networks : the official journal of the International Neural Network Society, 153:235-253 pii:S0893-6080(22)00213-1 [Epub ahead of print].

A brain-computer interface (BCI) enables a user to communicate directly with an external device, e.g., a computer, using brain signals. It can be used to research, map, assist, augment, or repair human cognitive or sensory-motor functions. A closed-loop BCI system performs signal acquisition, temporal filtering, spatial filtering, feature engineering and classification, before sending out the control signal to an external device. Transfer learning (TL) has been widely used in motor imagery (MI) based BCIs to reduce the calibration effort for a new subject, greatly increasing their utility. This tutorial describes how TL can be considered in as many components of a BCI system as possible, and introduces a complete TL pipeline for MI-based BCIs. Examples on two MI datasets demonstrated the advantages of considering TL in multiple components of MI-based BCIs. Especially, integrating data alignment and sophisticated TL approaches can significantly improve the classification performance, and hence greatly reduces the calibration effort.

RevDate: 2022-06-25

Jeon BB, Fuchs T, Chase SM, et al (2022)

Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task.

Nature communications, 13(1):3638.

Acquisition of new skills has the potential to disturb existing network function. To directly assess whether previously acquired cortical function is altered during learning, mice were trained in an abstract task in which selected activity patterns were rewarded using an optical brain-computer interface device coupled to primary visual cortex (V1) neurons. Excitatory neurons were longitudinally recorded using 2-photon calcium imaging. Despite significant changes in local neural activity during task performance, tuning properties and stimulus encoding assessed outside of the trained context were not perturbed. Similarly, stimulus tuning was stable in neurons that remained responsive following a different, visual discrimination training task. However, visual discrimination training increased the rate of representational drift. Our results indicate that while some forms of perceptual learning may modify the contribution of individual neurons to stimulus encoding, new skill learning is not inherently disruptive to the quality of stimulus representation in adult V1.

RevDate: 2022-06-24

Rubinos C, Kwon SB, Megjhani M, et al (2022)

Predicting Shunt Dependency from the Effect of Cerebrospinal Fluid Drainage on Ventricular Size.

Neurocritical care [Epub ahead of print].

BACKGROUND: Prolonged external ventricular drainage (EVD) in patients with subarachnoid hemorrhage (SAH) leads to morbidity, whereas early removal can have untoward effects related to recurrent hydrocephalus. A metric to help determine the optimal time for EVD removal or ventriculoperitoneal shunt (VPS) placement would be beneficial in preventing the prolonged, unnecessary use of EVD. This study aimed to identify whether dynamics of cerebrospinal fluid (CSF) biometrics can temporally predict VPS dependency after SAH.

METHODS: This was a retrospective analysis of a prospective, single-center, observational study of patients with aneurysmal SAH who required EVD placement for hydrocephalus. Patients were divided into VPS-dependent (VPS+) and non-VPS dependent groups. We measured the bicaudate index (BCI) on all available computed tomography scans and calculated the change over time (ΔBCI). We analyzed the relationship of ΔBCI with CSF output by using Pearson's correlation. A k-nearest neighbor model of the relationship between ΔBCI and CSF output was computed to classify VPS.

RESULTS: Fifty-eight patients met inclusion criteria. CSF output was significantly higher in the VPS+ group in the 7 days post EVD placement. There was a negative correlation between delta BCI and CSF output in the VPS+ group (negative delta BCI means ventricles become smaller) and a positive correlation in the VPS- group starting from days four to six after EVD placement (p < 0.05). A weighted k-nearest neighbor model for classification had a sensitivity of 0.75, a specificity of 0.70, and an area under the receiver operating characteristic curve of 0.80.

CONCLUSIONS: The correlation of ΔBCI and CSF output is a reliable intraindividual biometric for VPS dependency after SAH as early as days four to six after EVD placement. Our machine learning model leverages this relationship between ΔBCI and cumulative CSF output to predict VPS dependency. Early knowledge of VPS dependency could be studied to reduce EVD duration in many centers (intensive care unit length of stay).

RevDate: 2022-06-24

Tang CC, Huang JF, Kuo LW, et al (2022)

The highest troponin I level during admission is associated with mortality in blunt cardiac injury patients.

Injury pii:S0020-1383(22)00416-8 [Epub ahead of print].

BACKGROUND: Cardiac troponin I (cTnI) levels are usually measured in primary evaluations of blunt cardiac injury (BCI) patients. We evaluated the associations of cTnI levels with the outcomes of BCI patients at different times.

METHODS: From 2015 to 2019, blunt chest trauma patients with elevated cTnI levels were compared with patients without elevated cTnI levels using propensity score matching (PSM) to minimize selection bias. The cTnI levels at different times in the survivors and nonsurvivors were compared.

RESULTS: A total of 2,287 blunt chest trauma patients were included, and 57 (2.5%) of the patients had BCIs. PSM showed that patients with and without elevated cTnI levels had similar mortality rates (13.0% vs. 11.1%, p-value = 0.317], hospital lengths of stay (LOSs) [17.3 (14.4) vs. 15.5 (22.2) days, p-value = 0.699] and intensive care unit (ICU) LOSs [7.7 (12.1) vs. 6.4 (15.4) days, p-value = 0.072]. Among the BCI patients, nonsurvivors had a significantly higher highest cTnI level during the observation period than survivors. Additionally, patients who needed surgical intervention had significantly higher highest cTnI levels than patients who did not.

CONCLUSIONS: An elevated cTnI level is insufficient for the evaluation of BCI and the determination of the need for further treatment. The highest cTnI level during the observation period may be related to mortality and the need for surgery in BCI patients.

RevDate: 2022-06-24

Wang Q, Luo T, Xu X, et al (2022)

Chitosan-based composites reinforced with antibacterial flexible wood membrane for rapid hemostasis.

International journal of biological macromolecules pii:S0141-8130(22)01278-8 [Epub ahead of print].

Irregular hemorrhagic traumas always threaten the health of patients due to uncontrollable bleeding and wound infections. The traditional hemostatic materials show dissatisfactory hemostatic efficiency and antibacterial activity in solving these potential bleeding dangers. Herein, we proposed a kind of composites based on flexible wood membrane (FWM) loaded with chitosan/alginate derivative for accelerating rapid hemostasis and preventing infection. FWM was removed part of hemicellulose and lignin by using NaOH/Na2SO3 mixture to obtain excellent flexibility while retaining the original porous structure, followed by loading silver nanoparticles on the FWM surface to prepare AgNPs-FWM as an antibacterial bio-carrier. Then, AgNPs-FWM was coated with polyoxyethylene stearate-modified chitosan and multi-aldehyde sodium alginate to fabricate the composites of chitosan/alginate/AgNPs-FWM (CSA/AgNPs-FWM) using in-situ Schiff base reaction. Furthermore, in vitro and in vivo experiments showed that the CSA/AgNPs-FWM composites exhibited lower BCI value (2.6 ± 1.3 %), more rapid hemostasis (26 s) and lower blood loss (67.8 mg) than that of the traditional materials. The possible mechanism for the hemostasis process was not only the high blood absorption capacity, but also the synergistic interaction between hydrophobic alkane chains, amino groups, aldehydes, hydroxyl groups and blood cells. Moreover, CSA/AgNPs-FWM showed exceptional superiorities in mechanical properties and antibacterial activity, which endowed composites high potential in hemostasis application for irregular external wound.

RevDate: 2022-06-24

Ju C, C Guan (2022)

Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Deep learning (DL) has been widely investigated in a vast majority of applications in electroencephalography (EEG)-based brain-computer interfaces (BCIs), especially for motor imagery (MI) classification in the past five years. The mainstream DL methodology for the MI-EEG classification exploits the temporospatial patterns of EEG signals using convolutional neural networks (CNNs), which have been particularly successful in visual images. However, since the statistical characteristics of visual images depart radically from EEG signals, a natural question arises whether an alternative network architecture exists apart from CNNs. To address this question, we propose a novel geometric DL (GDL) framework called Tensor-CSPNet, which characterizes spatial covariance matrices derived from EEG signals on symmetric positive definite (SPD) manifolds and fully captures the temporospatiofrequency patterns using existing deep neural networks on SPD manifolds, integrating with experiences from many successful MI-EEG classifiers to optimize the framework. In the experiments, Tensor-CSPNet attains or slightly outperforms the current state-of-the-art performance on the cross-validation and holdout scenarios in two commonly used MI-EEG datasets. Moreover, the visualization and interpretability analyses also exhibit the validity of Tensor-CSPNet for the MI-EEG classification. To conclude, in this study, we provide a feasible answer to the question by generalizing the DL methodologies on SPD manifolds, which indicates the start of a specific GDL methodology for the MI-EEG classification.

RevDate: 2022-06-24

Needham JF, Arellano G, Davies SJ, et al (2022)

Tree crown damage and its effects on forest carbon cycling in a tropical forest.

Global change biology [Epub ahead of print].

Crown damage can account for over 23% of canopy biomass turnover in tropical forests and is a strong predictor of tree mortality, yet it is not typically represented in vegetation models. We incorporate crown damage into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to evaluate how lags between damage and tree recovery or death alter demographic rates and patterns of carbon turnover. We represent crown damage as a reduction in a tree's crown area and leaf and branch biomass, and allow associated variation in the ratio of aboveground to belowground plant tissue. We compare simulations with crown damage to simulations with equivalent instant increases in mortality and benchmark results against data from Barro Colorado Island (BCI), Panama. In FATES, crown damage causes decreases in growth rates that match observations from BCI. Crown damage leads to increases in carbon starvation mortality in FATES, but only in configurations with high root respiration and decreases in carbon storage following damage. Crown damage also alters competitive dynamics, as plant functional types that can recover from crown damage outcompete those that cannot. This is a first exploration of the trade-off between the additional complexity of the novel crown damage module and improved predictive capabilities. At BCI, a tropical forest that does not experience high levels of disturbance, both the crown damage simulations and simulations with equivalent increases in mortality do a reasonable job of capturing observations. The crown damage module provides functionality for exploring dynamics in forests with more extreme disturbances such as cyclones, and for capturing the synergistic effects of disturbances that overlap in space and time.

RevDate: 2022-06-24

Sapari L, Hout S, JY Chung (2022)

Brain Implantable End-Fire Antenna with Enhanced Gain and Bandwidth.

Sensors (Basel, Switzerland), 22(12): pii:s22124328.

An end-fire radiating implantable antenna with a small footprint and broadband operation at the frequency range of 3-5 GHz is proposed for high-data-rate wireless communication in a brain-machine interface. The proposed Vivaldi antenna was implanted vertically along the height of the skull to avoid deformation in the radiation pattern and to compensate for a gain-loss caused by surrounding lossy brain tissues. It was shown that the vertically implanted end-fire antenna had a 3 dB higher antenna gain than a horizontally implanted broadside radiating antenna discussed in recent literature. Additionally, comb-shaped slot arrays imprinted on the Vivaldi antenna lowered the resonant frequency by approximately 2 GHz and improved the antenna gain by more than 2 dB compared to an ordinary Vivaldi antenna. An antenna prototype was fabricated and then tested for verification inside a seven-layered semi-solid brain phantom where each layer had similar electromagnetic material properties as actual brain tissues. The measured data showed that the antenna radiated toward the end-fire direction with an average gain of -15.7 dBi under the frequency of interest, 3-5 GHz. A link budget analysis shows that reliable wireless communication can be achieved over a distance of 10.8 cm despite the electromagnetically harsh environment.

RevDate: 2022-06-24

Chang Z, Zhang C, C Li (2022)

Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network.

Micromachines, 13(6): pii:mi13060927.

For the successful application of brain-computer interface (BCI) systems, accurate recognition of electroencephalography (EEG) signals is one of the core issues. To solve the differences in individual EEG signals and the problem of less EEG data in classification and recognition, an attention mechanism-based multi-scale convolution network was designed; the transfer learning data alignment algorithm was then introduced to explore the application of transfer learning for analyzing motor imagery EEG signals. The data set 2a of BCI Competition IV was used to verify the designed dual channel attention module migration alignment with convolution neural network (MS-AFM). Experimental results showed that the classification recognition rate improved with the addition of the alignment algorithm and adaptive adjustment in transfer learning; the average classification recognition rate of nine subjects was 86.03%.

RevDate: 2022-06-23

Tremmel C, Fernandez-Vargas J, Stamos D, et al (2022)

A meta-learning BCI for estimating decision confidence.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of Brain-Computer Interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods.

APPROACH: We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from EEG and EOG data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants' data to produce a prediction algorithm that is then quickly tuned to new participants. We compared it with with the traditional single-subject training almost universally adopted in BCIs, a state-of-the-art transfer learning technique called Domain Adversarial Neural Networks (DANN), a transfer-learning adaptation of a zero-training method we used recently for a similar task, and with a simple baseline algorithm.

MAIN RESULTS: The meta-learning approach was significantly better than other approaches in most conditions, and much better in situations where limited data from a new participant are available for training/tuning. Meta-learning by biased regularisation allowed our BCI to seamlessly integrate information from past participants with data from a specific user to produce high-performance predictors. Its robustness in the presence of small training sets is a real-plus in BCI applications, as new users need to train the BCI for a much shorter period.

SIGNIFICANCE: Due to the variability and noise of EEG/EOG data, BCIs need to be normally trained with data from a specific participant. This work shows that even better performance can be obtained using our version of meta-learning by biased regularisation.

RevDate: 2022-06-23

Bodda S, S Diwakar (2022)

Exploring EEG spectral and temporal dynamics underlying a hand grasp movement.

PloS one, 17(6):e0270366 pii:PONE-D-21-37461.

For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex's functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.

RevDate: 2022-06-23

Li R, Wang L, Suganthan PN, et al (2022)

Sample-Based Data Augmentation Based on Electroencephalogram Intrinsic Characteristics.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve data efficiency. In addition, contrastive learning has recently been shown to hold great promise in learning effective representations without human supervision, which has the potential to improve the electroencephalogram-based recognition performance with limited labeled data. However, heavy data augmentation is a key ingredient of contrastive learning. In view of the limited number of sample-based data augmentation in electroencephalogram processing, three methods, performance-measure-based time warp, frequency noise addition and frequency masking, are proposed based on the characteristics of electroencephalogram signal. These methods are parameter learning free, easy to implement, and can be applied to individual samples. In the experiment, the proposed data augmentation methods are evaluated on three electroencephalogram-based classification tasks, including situation awareness recognition, motor imagery classification and brain-computer interface steady-state visually evoked potentials speller system. Results demonstrated that the convolutional models trained with the proposed data augmentation methods yielded significantly improved performance over baselines. In overall, this work provides more potential methods to cope with the problem of limited data and boost the classification performance in electroencephalogram processing.

RevDate: 2022-06-23

Marzuki I, Septiningsih E, Kaseng ES, et al (2022)

Investigation of Global Trends of Pollutants in Marine Ecosystems around Barrang Caddi Island, Spermonde Archipelago Cluster: An Ecological Approach.

Toxics, 10(6): pii:toxics10060301.

High-quality marine ecosystems are free from global trending pollutants' (GTP) contaminants. Accuracy and caution are needed during the exploitation of marine resources during marine tourism to prevent future ecological hazards that cause chain effects on aquatic ecosystems and humans. This article identifies exposure to GTP: microplastic (MP); polycyclic aromatic hydrocarbons (PAH); pesticide residue (PR); heavy metal (HM); and medical waste (MW), in marine ecosystems in the marine tourism area (MTA) area and Barrang Caddi Island (BCI) waters. A combination of qualitative and quantitative analysis methods were used with analytical instruments and mathematical formulas. The search results show the average total abundance of MPs in seawater (5.47 units/m3) and fish samples (7.03 units/m3), as well as in the sediment and sponge samples (8.18 units/m3) and (8.32 units/m3). Based on an analysis of the polymer structure, it was identified that the dominant light group was MPs: polyethylene (PE); polypropylene (PP); polystyrene (PS); followed by polyamide-nylon (PA); and polycarbonate (PC). Several PAH pollutants were identified in the samples. In particular, naphthalene (NL) types were the most common pollutants in all of the samples, followed by pyrene (PN), and azulene (AZ). Pb+2 and Cu+2 pollutants around BCI were successfully calculated, showing average concentrations in seawater of 0.164 ± 0.0002 mg/L and 0.293 ± 0.0007 mg/L, respectively, while in fish, the concentrations were 1.811 ± 0.0002 µg/g and 4.372 ± 0.0003 µg/g, respectively. Based on these findings, the BCI area is not recommended as a marine tourism destination.

RevDate: 2022-06-23

Wang J, Chen YH, Yang J, et al (2022)

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern.

Biosensors, 12(6): pii:bios12060384.

To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a "follow-up" pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.

RevDate: 2022-06-22

Guo N, Wang X, Duanmu D, et al (2022)

SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to improve post-stroke hand function. It is necessary for a user of MI-BCI to receive a long time training, while the user usually suffers unsuccessful and unsatisfying results in the beginning. To propose another non-invasive BCI paradigm rather than MI-BCI, steady-state visually evoked potentials (SSVEP) based BCI was proposed as user intension detection to trigger the soft robotic glove for post-stroke hand function rehabilitation. Thirty post-stroke patients with impaired hand function were randomly and equally divided into three groups to receive conventional, robotic, and BCI-robotic therapy in this randomized control trial (RCT). Clinical assessment of Fugl-Meyer Motor Assessment of Upper Limb (FMA-UL), Wolf Motor Function Test (WMFT) and Modified Ashworth Scale (MAS) were performed at pre-training, post-training and three months follow-up. In comparing to other groups, The BCI-robotic group showed significant improvement after training in FMA full score (10.05±8.03, p=0.001), FMA shoulder/elbow (6.2±5.94, p=0.0004) and FMA wrist/hand (4.3±2.83, p=0.007), and WMFT (5.1±5.53, p=0.037). The improvement of FMA was significantly correlated with BCI accuracy (r=0.714, p=0.032). Recovery of hand function after rehabilitation of SSVEP-BCI controlled soft robotic glove showed better result than solely robotic glove rehabilitation, equivalent efficacy as results from previous reported MI-BCI robotic hand rehabilitation. It proved the feasibility of SSVEP-BCI controlled soft robotic glove in post-stroke hand function rehabilitation.

RevDate: 2022-06-22

Li M, Wu L, Xu G, et al (2022)

A Robust 3D-Convolutional Neural Network-Based Electroencephalogram Decoding Model for the Intra-Individual Difference.

International journal of neural systems [Epub ahead of print].

The convolutional neural network (CNN) has emerged as a powerful tool for decoding electroencephalogram (EEG), which owns the potential use in the event-related potential-based brain-computer interface (ERP-BCI). However, the intra-individual difference of ERP makes the traditional learning models trained on static EEG data hard to decode when the EEG features vary along the time, which limits the long-time performance of the model. Addressing this problem, this study proposes a three-dimension CNN (3D-CNN)-based model to decode the ERPs dynamically. As input, the EEG is transformed into a brain topographic map stream along time. Then the 3D-CNN applies three-dimension kernels to capture the dynamical characteristic of spatial feature at several time points. Ten subjects participated in a cross-time task for 6 or 12[Formula: see text]h. The 3D-CNN shows higher accuracies and shorter computational cost than the baseline models of the 2D-CNN, the long short term memory (LSTM), the back propagation (BP), and the fisher linear discriminant analysis (FLDA) when detecting the ERPs. In addition, four schemes of the 3D-CNN are compared to explore the influence of the structure on the performance. This result demonstrates advanced robustness of the 3D-CNN kernel to the intra-individual EEG difference, helping to launch a more practical EEG decoding model for a long-time use.

RevDate: 2022-06-22

Ou Y, Sun S, Gan H, et al (2022)

An improved self-supervised learning for EEG classification.

Mathematical biosciences and engineering : MBE, 19(7):6907-6922.

Motor Imagery EEG (MI-EEG) classification plays an important role in different Brain-Computer Interface (BCI) systems. Recently, deep learning has been widely used in the MI-EEG classification tasks, however this technology requires a large number of labeled training samples which are difficult to obtain, and insufficient labeled training samples will result in a degradation of the classification performance. To address the degradation problem, we investigate a Self-Supervised Learning (SSL) based MI-EEG classification method to reduce the dependence on a large number of labeled training samples. The proposed method includes a pretext task and a downstream classification one. In the pretext task, each MI-EEG is rearranged according to the temporal characteristic. A network is pre-trained using the original and rearranged MI-EEGs. In the downstream task, a MI-EEG classification network is firstly initialized by the network learned in the pretext task and then trained using a small number of the labeled training samples. A series of experiments are conducted on Data sets 1 and 2b of BCI competition IV and IVa of BCI competition III. In the case of one third of the labeled training samples, the proposed method can obtain an obvious improvement compared to the baseline network without using SSL. In the experiments under different percentages of the labeled training samples, the results show that the designed SSL strategy is effective and beneficial to improving the classification performance.

RevDate: 2022-06-20

Nickl RW, Anaya MA, Thomas TM, et al (2022)

Characteristics and stability of sensorimotor activity driven by isolated-muscle group activation in a human with tetraplegia.

Scientific reports, 12(1):10353.

Understanding the cortical representations of movements and their stability can shed light on improved brain-machine interface (BMI) approaches to decode these representations without frequent recalibration. Here, we characterize the spatial organization (somatotopy) and stability of the bilateral sensorimotor map of forearm muscles in an incomplete-high spinal-cord injury study participant implanted bilaterally in the primary motor and sensory cortices with Utah microelectrode arrays (MEAs). We built representation maps by recording bilateral multiunit activity (MUA) and surface electromyography (EMG) as the participant executed voluntary contractions of the extensor carpi radialis (ECR), and attempted motions in the flexor carpi radialis (FCR), which was paralytic. To assess stability, we repeatedly mapped and compared left- and right-wrist-extensor-related activity throughout several sessions, comparing somatotopy of active electrodes, as well as neural signals both at the within-electrode (multiunit) and cross-electrode (network) levels. Wrist motions showed significant activation in motor and sensory cortical electrodes. Within electrodes, firing strength stability diminished as the time increased between consecutive measurements (hours within a session, or days across sessions), with higher stability observed in sensory cortex than in motor, and in the contralateral hemisphere than in the ipsilateral. However, we observed no differences at network level, and no evidence of decoding instabilities for wrist EMG, either across timespans of hours or days, or across recording area. While map stability differs between brain area and hemisphere at multiunit/electrode level, these differences are nullified at ensemble level.

RevDate: 2022-06-20

Liu D, Li S, Ren L, et al (2022)

The superior colliculus/lateral posterior thalamic nuclei in mice rapidly transmit fear visual information through the theta frequency band.

Neuroscience pii:S0306-4522(22)00313-X [Epub ahead of print].

Animals perceive threat information mainly from vision, and the subcortical visual pathway plays a critical role in the rapid processing of fear visual information. The superior colliculus (SC) and lateral posterior (LP) nuclei of the thalamus are key components of the subcortical visual pathway; however, how animals encode and transmit fear visual information is unclear. To evaluate the response characteristics of neurons in SC and LP thalamic nuclei under fear visual stimuli, extracellular action potentials (spikes) and local field potential signals were recorded under looming and dimming visual stimuli. The results showed that both SC and LP thalamic nuclei were strongly responsive to looming visual stimuli but not sensitive to dimming visual stimuli. Under the looming visual stimulus, the theta (θ) frequency bands of both nuclei showed obvious oscillations, which markedly enhanced the synchronization between neurons. The functional network characteristics also indicated that the network connection density and information transmission efficiency were higher under fear visual stimuli. These findings suggest that both SC and LP thalamic nuclei can effectively identify threatening fear visual information and rapidly transmit it between nuclei through the θ frequency band. This discovery can provide a basis for subsequent coding and decoding studies in the subcortical visual pathways.

RevDate: 2022-06-20

Fan L, Shen H, Xie F, et al (2022)

DC-tCNN: A Deep Model for EEG-based Detection of Dim Targets.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

OBJECTIVE: Dim target detection in remote sensing images is a significant and challenging problem. In this work, we seek to explore event-related brain responses of dim target detection tasks and extend the brain-computer interface (BCI) systems to this task for efficiency enhancement.

METHODS: We develop a BCI paradigm named Asynchronous Visual Evoked Paradigm (AVEP), in which subjects are required to search the dim targets within satellite images when their scalp electroencephalography (EEG) signals are simultaneously recorded. In the paradigm, stimulus onset time and target onset time are asynchronous because subjects need enough time to confirm whether there are targets of interest in the presented serial images. We further propose a Domain adaptive and Channel-wise attention-based Time-domain Convolutional Neural Network (DC-tCNN) to solve the single-trial EEG classification problem for the AVEP task. In this model, we design a multi-scale CNN module combined with a channel-wise attention module to effectively extract event-related brain responses underlying EEG signals. Meanwhile, domain adaptation is proposed to mitigate cross-subject distribution discrepancy.

RESULTS: The results demonstrate the superior performance and better generalizability of this model in classifying the single-trial EEG data of AVEP task in contrast to typical EEG deep learning networks. Visualization analyses of spatiotemporal features also illustrate the effectiveness and interpretability of our proposed paradigm and learning model.

CONCLUSION: The proposed paradigm and model can effectively explore ambiguous event-related brain responses on EEG-based dim target detection tasks.

SIGNIFICANCE: Our work can provide a valuable reference for BCI-based image detection of dim targets.

RevDate: 2022-06-20

Riccio A, Schettini F, Galiotta V, et al (2022)

Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis.

Frontiers in human neuroscience, 16:868419.

Brain-computer interface (BCI) can provide people with motor disabilities with an alternative channel to access assistive technology (AT) software for communication and environmental interaction. Multiple sclerosis (MS) is a chronic disease of the central nervous system that mostly starts in young adulthood and often leads to a long-term disability, possibly exacerbated by the presence of fatigue. Patients with MS have been rarely considered as potential BCI end-users. In this pilot study, we evaluated the usability of a hybrid BCI (h-BCI) system that enables both a P300-based BCI and conventional input devices (i.e., muscular dependent) to access mainstream applications through the widely used AT software for communication "Grid 3." The evaluation was performed according to the principles of the user-centered design (UCD) with the aim of providing patients with MS with an alternative control channel (i.e., BCI), potentially less sensitive to fatigue. A total of 13 patients with MS were enrolled. In session I, participants were presented with a widely validated P300-based BCI (P3-speller); in session II, they had to operate Grid 3 to access three mainstream applications with (1) an AT conventional input device and (2) the h-BCI. Eight patients completed the protocol. Five out of eight patients with MS were successfully able to access the Grid 3 via the BCI, with a mean online accuracy of 83.3% (± 14.6). Effectiveness (online accuracy), satisfaction, and workload were comparable between the conventional AT inputs and the BCI channel in controlling the Grid 3. As expected, the efficiency (time for correct selection) resulted to be significantly lower for the BCI with respect to the AT conventional channels (Z = 0.2, p < 0.05). Although cautious due to the limited sample size, these preliminary findings indicated that the BCI control channel did not have a detrimental effect with respect to conventional AT channels on the ability to operate an AT software (Grid 3). Therefore, we inferred that the usability of the two access modalities was comparable. The integration of BCI with commercial AT input devices to access a widely used AT software represents an important step toward the introduction of BCIs into the AT centers' daily practice.

RevDate: 2022-06-20

Al Boustani G, Weiß LJK, Li H, et al (2022)

Influence of Auditory Cues on the Neuronal Response to Naturalistic Visual Stimuli in a Virtual Reality Setting.

Frontiers in human neuroscience, 16:809293.

Virtual reality environments offer great opportunities to study the performance of brain-computer interfaces (BCIs) in real-world contexts. As real-world stimuli are typically multimodal, their neuronal integration elicits complex response patterns. To investigate the effect of additional auditory cues on the processing of visual information, we used virtual reality to mimic safety-related events in an industrial environment while we concomitantly recorded electroencephalography (EEG) signals. We simulated a box traveling on a conveyor belt system where two types of stimuli - an exploding and a burning box - interrupt regular operation. The recordings from 16 subjects were divided into two subsets, a visual-only and an audio-visual experiment. In the visual-only experiment, the response patterns for both stimuli elicited a similar pattern - a visual evoked potential (VEP) followed by an event-related potential (ERP) over the occipital-parietal lobe. Moreover, we found the perceived severity of the event to be reflected in the signal amplitude. Interestingly, the additional auditory cues had a twofold effect on the previous findings: The P1 component was significantly suppressed in the case of the exploding box stimulus, whereas the N2c showed an enhancement for the burning box stimulus. This result highlights the impact of multisensory integration on the performance of realistic BCI applications. Indeed, we observed alterations in the offline classification accuracy for a detection task based on a mixed feature extraction (variance, power spectral density, and discrete wavelet transform) and a support vector machine classifier. In the case of the explosion, the accuracy slightly decreased by -1.64% p. in an audio-visual experiment compared to the visual-only. Contrarily, the classification accuracy for the burning box increased by 5.58% p. when additional auditory cues were present. Hence, we conclude, that especially in challenging detection tasks, it is favorable to consider the potential of multisensory integration when BCIs are supposed to operate under (multimodal) real-world conditions.

RevDate: 2022-06-21
CmpDate: 2022-06-21

Hu X, Liu Y, Zhang HL, et al (2022)

Noninvasive Human-Computer Interface Methods and Applications for Robotic Control: Past, Current, and Future.

Computational intelligence and neuroscience, 2022:1635672.

The purpose of this study is to explore the noninvasive human-computer interaction methods that have been widely used in various fields, especially in the field of robot control. To have a deep understanding of the development of the methods, this paper employs "Mapping Knowledge Domains" (MKDs) to find research hotspots in the area to show the future potential development. Through the literature review, this paper found that there was a paradigm shift in the research of noninvasive BCI technologies for robotic control, which has occurred from early 2010 since the rapid development of machine learning, deep learning, and sensory technologies. This study further provides a trend analysis that the combination of data-driven methods with optimized algorithms and human-sensory-driven methods will be the key areas for the future noninvasive method development in robotic control. Based on the above findings, the paper provides a potential developing way of noninvasive HCI methods for related areas including health care, robotic system, and media.

RevDate: 2022-06-20

Liu X, Liu B, Dong G, et al (2022)

Facilitating Applications of SSVEP-Based BCIs by Within-Subject Information Transfer.

Frontiers in neuroscience, 16:863359.

The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) can provide high-speed alternative and augmentative communication in real-world applications. For individuals using a long-term BCI, within-subject (i.e., cross-day and cross-electrode) transfer learning could improve the BCI performance and reduce the calibration burden. To validate the within-subject transfer learning scheme, this study designs a 40-target SSVEP-BCI. Sixteen subjects are recruited, each of whom has performed experiments on three different days and has undergone the experiments of the SSVEP-BCIs based on the dry and wet electrodes. Several transfer directions, including the cross-day directions in parallel with the cross-electrode directions, are analyzed, and it is found that the transfer learning-based approach can maintain stable performance by zero training. Compared with the fully calibrated approaches, the transfer learning-based approach can achieve significantly better or comparable performance in different transfer directions. This result verifies that the transfer learning-based scheme is well suited for implementing a high-speed zero-training SSVEP-BCI, especially the dry electrode-based SSVEP-BCI system. A validation experiment of the cross-day wet-to-dry transfer, involving nine subjects, has shown that the average accuracy is 85.97 ± 5.60% for the wet-to-dry transfer and 77.69 ± 6.42% for the fully calibrated method with dry electrodes. By leveraging the electroencephalography data acquired on different days by different electrodes via transfer learning, this study lays the foundation for facilitating the long-term usage of the SSVEP-BCI and advancing the frontier of the dry electrode-based SSVEP-BCI in real-world applications.

RevDate: 2022-06-20

Lu R, Zeng Y, Zhang R, et al (2022)

SAST-GCN: Segmentation Adaptive Spatial Temporal-Graph Convolutional Network for P3-Based Video Target Detection.

Frontiers in neuroscience, 16:913027.

Detecting video-induced P3 is crucial to building the video target detection system based on the brain-computer interface. However, studies have shown that the brain response patterns corresponding to video-induced P3 are dynamic and determined by the interaction of multiple brain regions. This paper proposes a segmentation adaptive spatial-temporal graph convolutional network (SAST-GCN) for P3-based video target detection. To make full use of the dynamic characteristics of the P3 signal data, the data is segmented according to the processing stages of the video-induced P3, and the brain network connections are constructed correspondingly. Then, the spatial-temporal feature of EEG data is extracted by adaptive spatial-temporal graph convolution to discriminate the target and non-target in the video. Especially, a style-based recalibration module is added to select feature maps with higher contributions and increase the feature extraction ability of the network. The experimental results demonstrate the superiority of our proposed model over the baseline methods. Also, the ablation experiments indicate that the segmentation of data to construct the brain connection can effectively improve the recognition performance by reflecting the dynamic connection relationship between EEG channels more accurately.

RevDate: 2022-06-20

Wang XY, Li C, Zhang R, et al (2022)

Intelligent Extraction of Salient Feature From Electroencephalogram Using Redundant Discrete Wavelet Transform.

Frontiers in neuroscience, 16:921642.

At present, electroencephalogram (EEG) signals play an irreplaceable role in the diagnosis and treatment of human diseases and medical research. EEG signals need to be processed in order to reduce the adverse effects of irrelevant physiological process interference and measurement noise. Wavelet transform (WT) can provide a time-frequency representation of a dynamic process, and it has been widely utilized in salient feature analysis of EEG. In this paper, we investigate the problem of translation variability (TV) in discrete wavelet transform (DWT), which causes degradation of time-frequency localization. It will be verified through numerical simulations that TV is caused by downsampling operations in decomposition process of DWT. The presence of TV may cause severe distortions of features in wavelet subspaces. However, this phenomenon has not attracted much attention in the scientific community. Redundant discrete wavelet transform (RDWT) is derived by eliminating the downsampling operation. RDWT enjoys the attractive merit of translation invariance. RDWT shares the same time-frequency pattern with that of DWT. The discrete delta impulse function is used to test the time-frequency response of DWT and RDWT in wavelet subspaces. The results show that DWT is very sensitive to the translation of delta impulse function, while RDWT keeps the decomposition results unchanged. This conclusion has also been verified again in decomposition of actual EEG signals. In conclusion, to avoid possible distortions of features caused by translation sensitivity in DWT, we recommend the use of RDWT with more stable performance in BCI research and clinical applications.

RevDate: 2022-06-20

Deddah T, Styner M, J Prieto (2022)

Local Extraction of Extra-Axial CSF from structural MRI.

Proceedings of SPIE--the International Society for Optical Engineering, 12036:.

The quantification of cerebrospinal fluid (CSF), specifically the extra-axial cerebrospinal fluid (EA-CSF), which is the CSF in the subarachnoid space surrounding the cortical surface of the brain, has recently been shown to play an important role in the neuropathology of autism spectrum disorder (ASD) in infants. While prior work addressed measuring the global volume of EA-CSF, there was no available tool that quantifies the local, anatomical distribution of the EA-CSF. A localized EA-CSF quantification would provide more accurate and interpretable measurements. In our recent work, we proposed such a local EA-CSF extraction by using a pipeline that combines probabilistic brain tissue segmentation, cortical surface reconstruction and streamline-based local EA-CSF quantification. Yet, that system had several shortcomings, in particular a lack of available software tools, as well as a quantification where EA-CSF portions are counted multiple times. The purpose of this article is to present a novel, graphical user interface based, publicly available software tool, called LocalEACSF, which allows the user to easily run an adapted version of this pipeline and provide a set of straightforward quality control visualizations to assess the quality of the EA-CSF quantification. This tool further adds improvements and optimizations to the prior assessment. The LocalEACSF tool allows neuroimaging labs to compute a local extraction of extra-axial CSF in their neuroimaging studies in order to investigate its role in normal and atypical brain development, without the need for extensive technical knowledge.

RevDate: 2022-06-19

Liu M, J Ushiba (2022)

Brain-machine Interface (BMI)-based Neurorehabilitation for Post-stroke Upper Limb Paralysis.

The Keio journal of medicine [Epub ahead of print].

Because recovery from upper limb paralysis after stroke is challenging, compensatory approaches have been the main focus of upper limb rehabilitation. However, based on fundamental and clinical research indicating that the brain has a far greater potential for plastic change than previously thought, functional restorative approaches have become increasingly common. Among such interventions, constraint-induced movement therapy, task-specific training, robotic therapy, neuromuscular electrical stimulation (NMES), mental practice, mirror therapy, and bilateral arm training are recommended in recently published stroke guidelines. For severe upper limb paralysis, however, no effective therapy has yet been established. Against this background, there is growing interest in applying brain-machine interface (BMI) technologies to upper limb rehabilitation. Increasing numbers of randomized controlled trials have demonstrated the effectiveness of BMI neurorehabilitation, and several meta-analyses have shown medium to large effect sizes with BMI therapy. Subgroup analyses indicate higher intervention effects in the subacute group than the chronic group, when using movement attempts as the BMI-training trigger task rather than using motor imagery, and using NMES as the external device compared with using other devices. The Keio BMI team has developed an electroencephalography-based neurorehabilitation system and has published clinical and basic studies demonstrating its effectiveness and neurophysiological mechanisms. For its wider clinical application, the positioning of BMI therapy in upper limb rehabilitation needs to be clarified, BMI needs to be commercialized as an easy-to-use and cost-effective medical device, and training systems for rehabilitation professionals need to be developed. A technological breakthrough enabling selective modulation of neural circuits is also needed.

RevDate: 2022-06-21
CmpDate: 2022-06-21

Asadzadeh S, Yousefi Rezaii T, Beheshti S, et al (2022)

Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes.

Scientific reports, 12(1):10282.

Due to the effect of emotions on interactions, interpretations, and decisions, automatic detection and analysis of human emotions based on EEG signals has an important role in the treatment of psychiatric diseases. However, the low spatial resolution of EEG recorders poses a challenge. In order to overcome this problem, in this paper we model each emotion by mapping from scalp sensors to brain sources using Bernoulli-Laplace-based Bayesian model. The standard low-resolution electromagnetic tomography (sLORETA) method is used to initialize the source signals in this algorithm. Finally, a dynamic graph convolutional neural network (DGCNN) is used to classify emotional EEG in which the sources of the proposed localization model are considered as the underlying graph nodes. In the proposed method, the relationships between the EEG source signals are encoded in the DGCNN adjacency matrix. Experiments on our EEG dataset recorded at the Brain-Computer Interface Research Laboratory, University of Tabriz as well as publicly available SEED and DEAP datasets show that brain source modeling by the proposed algorithm significantly improves the accuracy of emotion recognition, such that it achieve a classification accuracy of 99.25% during the classification of the two classes of positive and negative emotions. These results represent an absolute 1-2% improvement in terms of classification accuracy over subject-dependent and subject-independent scenarios over the existing approaches.

RevDate: 2022-06-18

Maÿe A, Mutz M, AK Engel (2022)

Training the spatially-coded SSVEP BCI on the fly.

Journal of neuroscience methods pii:S0165-0270(22)00179-0 [Epub ahead of print].

BACKGROUND: The spatially-coded SSVEP BCI employs the retinotopic map in the human visual pathway to infer the gaze direction of the operator relative to a flicker stimulus inducing steady-state visual evoked potentials (SSVEPs) in the brain. It has been shown that with this method, up to 16 channels can be encoded using only a single flicker stimulus. Another advantage over conventional frequency-coded SSVEP BCIs, in which channels are encoded by different combinations of frequencies and phases, is that the operator does not have to gaze directly at flickering lights. This can reduce visual fatigue and improve user comfort. Whereas the frequency of the SSVEP response is well predictable, which has enabled the development of frequency-coded SSVEP BCIs which do not require training data, the spatial distribution of the SSVEP response over the scalp differs much more between different people. This requires collecting a substantial amount of training data before the spatially-coded BCI could be put into operation.

NEW METHOD: In this study we address this issue by combining the spatially-coded BCI with a feedback channel which the operator uses to flag classification errors, and which allows the system to accumulate valid training data while the BCI is used to solve a spatial navigation task.

RESULTS: Starting from the minimal number of samples required by the classification method, the approach achieved an average accuracy of 69 ± 15%, corresponding to an ITR of 31 ± 17 bits/min, in solving the task for the first time. This accuracy improved to 87 ± 9% (ITR: 54 ± 14 bits/min) after completing the task 2 more times. Further we show that participants with a stable SSVEP topography over repeated stimulation enable the BCI to achieve higher accuracies.

Compared to a similar system with separate training and application phases, the time to achieve the same output is reduced by more than 50 %.

CONCLUSIONS: Evaluating the approach in 17 participants suggests that the performance of the spatially-coded BCI with a minimal set of training samples is sufficient to be operational, and that performance keeps improving in the course of its application.

RevDate: 2022-06-17

Luis-Islas J, Luna M, Floran B, et al (2022)

Optoception: perception of optogenetic brain perturbations.

eNeuro pii:ENEURO.0216-22.2022 [Epub ahead of print].

How do animals experience brain manipulations? Optogenetics has allowed us to manipulate selectively and interrogate neural circuits underlying brain function in health and disease. However, little is known about whether mice can detect and learn from arbitrary optogenetic perturbations from a wide range of brain regions to guide behavior. To address this issue, mice were trained to report optogenetic brain perturbations to obtain rewards and avoid punishments. Here we found that mice can perceive optogenetic manipulations regardless of the perturbed brain area, rewarding effects, or the stimulation of glutamatergic, GABAergic, and dopaminergic cell types. We named this phenomenon optoception, a perceptible signal internally generated from perturbing the brain, as occurs with interoception. Using optoception, mice can learn to execute two different sets of instructions based on the laser frequency. Importantly, optoception can occur either activating or silencing a single cell type. Moreover, stimulation of two brain regions in a single mouse uncovered that the optoception induced by one brain region does not necessarily transfer to a second not previously stimulated area, suggesting a different sensation is experienced from each site. After learning, they can indistinctly use randomly interleaved perturbations from both brain regions to guide behavior. Collectively taken, our findings revealed that mice's brains could "monitor" perturbations of their self-activity, albeit indirectly, perhaps via interoception or as a discriminative stimulus, opening a new way to introduce information to the brain and control brain-computer interfaces.Significance StatementWe propose that most optogenetic brain manipulations may serve as a conditioned cue to guide behavioral decisions and learning, probably using a variety of either interoception, percepts, or other sensory/motor responses evoked by perturbing distinct brain circuits. Further research should uncover whether optoception is a fundamental property everywhere in the brain and unveil its underlying mechanisms.

RevDate: 2022-06-17

Ye X, Yang C, Chen Y, et al (2022)

Multi-Symbol Time Division Coding for High-Frequency Steady-State Visual Evoked Potential-Based Brain-Computer Interface.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

The optimization of coding stimulus is a cruial factor in the study of steady-state visual evoked potential (SSVEP)-based brain-computer interface(BCI).This study proposed an encoding approach named Multi-Symbol Time Division Coding (MSTDC). This approach is based on a protocol of maximizing the distance between neural responses, which aims to encode stimulation systems implementing any number of targets with finite stimulations of different frequencies and phases. Firstly, this study designed an SSVEP-based BCI system containing forty targets with this approach. The stimulation encoding of this system was achieved with four temporal-divided stimuli that adopt the same frequency of 30Hz and different phases. During the online experiments of twelve subjects, this system achieved an average accuracy of 96.77±2.47% and an average information transfer rate (ITR) of 119.05±6.11 bits/min. This study also devised an SSVEP-based BCI system containing 72 targets and proposed a Template Splicing task-related component analysis (TRCA) algorithm that utilized the dataset of the previous system containing forty targets as the training dataset. The subjects acquired an average accuracy of 86.23±7.75% and an average ITR of 95.68±14.19 bits/min. It can be inferred that MSTDC can encode multiple targets with limited frequencies and phases of stimuli. Meanwhile, this protocol can be effortlessly expanded into other systems and sufficiently reduce the cost of collecting training data. This study provides a feasible technique for obtaining a comfortable SSVEP-based BCI with multiple targets while maintaining high information transfer rate.

RevDate: 2022-06-17

Tsai BY, Diddi SVS, Ko LW, et al (2022)

Development of an Adaptive Artifact Subspace Reconstruction Based on Hebbian/Anti-Hebbian Learning Networks for Enhancing BCI Performance.

IEEE transactions on neural networks and learning systems, PP: [Epub ahead of print].

Brain-computer interface (BCI) actively translates the brain signals into executable actions by establishing direct communication between the human brain and external devices. Recording brain activity through electroencephalography (EEG) is generally contaminated with both physiological and nonphysiological artifacts, which significantly hinders the BCI performance. Artifact subspace reconstruction (ASR) is a well-known statistical technique that automatically removes artifact components by determining the rejection threshold based on the initial reference EEG segment in multichannel EEG recordings. In real-world applications, the fixed threshold may limit the efficacy of the artifact correction, especially when the quality of the reference data is poor. This study proposes an adaptive online ASR technique by integrating the Hebbian/anti-Hebbian neural networks into the ASR algorithm, namely, principle subspace projection ASR (PSP-ASR) and principal subspace whitening ASR (PSW-ASR) that segmentwise self-organize the artifact subspace by updating the synaptic weights according to the Hebbian and anti-Hebbian learning rules. The effectiveness of the proposed algorithm is compared to the conventional ASR approaches on benchmark EEG dataset and three BCI frameworks, including steady-state visual evoked potential (SSVEP), rapid serial visual presentation (RSVP), and motor imagery (MI) by evaluating the root-mean-square error (RMSE), the signal-to-noise ratio (SNR), the Pearson correlation, and classification accuracy. The results demonstrated that the PSW-ASR algorithm effectively removed the EEG artifacts and retained the activity-specific brain signals compared to the PSP-ASR, standard ASR (Init-ASR), and moving-window ASR (MW-ASR) methods, thereby enhancing the SSVEP, RSVP, and MI BCI performances. Finally, our empirical results from the PSW-ASR algorithm suggested the choice of an aggressive cutoff range of c = 1-10 for activity-specific BCI applications and a moderate range of for the benchmark dataset and general BCI applications.

RevDate: 2022-06-17

Venkatesh S, Miranda ER, E Braund (2022)

SSVEP-based Brain-computer Interface for Music using a Low-density EEG System.

Assistive technology : the official journal of RESNA [Epub ahead of print].

In this paper, we present a bespoke brain-computer interface (BCI), which was developed for a person with severe motor-impairments, who was previously a Violinist, to allow performing and composing music at home. It uses steady-state visually evoked potential (SSVEP) and adopts a dry, low-density, and wireless electroencephalogram (EEG) headset. In this study, we investigated two parameters: (1) placement of the EEG headset and (2) inter-stimulus distance and found that the former significantly improved the information transfer rate (ITR). To analyse EEG, we adopted canonical correlation analysis (CCA) without weight-calibration. The BCI for musical performance realised a high ITR of 37.59 ± 9.86 bits min-1 and mean accuracy of 88.89 ± 10.09%. The BCI for musical composition obtained an ITR of 14.91 ± 2.87 bits min-1 and a mean accuracy of 95.83 ± 6.97%. The BCI was successfully deployed to the person with severe motor-impairments. She regularly uses it for musical composition at home, demonstrating how BCIs can be translated from laboratories to real-world scenarios.

RevDate: 2022-06-17

Birch N, Graham J, Ozolins C, et al (2022)

Home-Based EEG Neurofeedback Intervention for the Management of Chronic Pain.

Frontiers in pain research (Lausanne, Switzerland), 3:855493.

Background: Chronic pain and associated symptoms often cause significant disability and reduced quality of life (QoL). Neurofeedback (NFB) as part of a Brain Computer Interface can help some patients manage chronic pain by normalising maladaptive brain activity measured with electroencephalography (EEG).

Objectives: This study was designed to assess the efficacy and safety of a novel home-based NFB device for managing chronic pain by modifying specific EEG activity.

Methods: A prospective, single-arm, proof-of-concept study was conducted between June 2020 and March 2021 among adults with chronic pain (registered with ClinicalTrials.gov NCT04418362). Axon EEG NFB systems for home use were provided to each, and 32-48 NFB training sessions were completed by the participants over 8-weeks. The primary outcome was self-reported pain. Assessment of central sensitisation, sleep quality, affective symptoms, change in QoL, adverse events during use and EEG correlations with symptoms were secondary outcomes.

Results: Sixteen participants were enrolled. Eleven reported pain relief following NFB training, eight reporting clinically significant improvements. Central sensitisation symptoms improved by a third (p < 0.0001), sleep quality by almost 50% (p < 0.001), anxiety reduced by 40% (p = 0.015), and QoL improved at final follow-up for 13 participants. The majority (69%) of participants who upregulated relative alpha reported improved pain, and those who downregulated relative hi-beta reported improved pain, reduced anxiety and depression scores. There were no adverse events during the trial.

Conclusions: Home-based NFB training is well-tolerated and may provide relief for sufferers of chronic pain and its associated symptoms.

Summary: Axon, a home-based NFB training device, can positively influence pain and associated symptoms in a proportion of people with chronic pain.

RevDate: 2022-06-15

Rustamov N, Humphries J, Carter A, et al (2022)

Theta-gamma coupling as a cortical biomarker of brain-computer interface-mediated motor recovery in chronic stroke.

Brain communications, 4(3):fcac136 pii:fcac136.

Chronic stroke patients with upper-limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain-computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain-computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm-specific mechanism would correlate with the functional improvements effected by a brain-computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain-computer interface therapy are associated with alterations in phase-amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain-computer interface task for 12 weeks. Resting-state EEG recordings and motor function data were acquired before initiating brain-computer interface therapy and once every 4 weeks after the therapy. Changes in phase-amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta-gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain-computer interface therapy sessions. Importantly, an increase in theta-gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta-gamma coupling increase following brain-computer interface therapy were mostly located in the hand regions of the primary motor cortex on the left and right cerebral hemispheres. Beta-gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha-gamma coupling was not altered by brain-computer interface therapy. Power spectra did not change significantly over the course of the brain-computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain-computer interface therapy was strongly correlated with increased theta-gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross-frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.

RevDate: 2022-06-14

Ying J, Wei Q, X Zhou (2022)

Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs.

Scientific reports, 12(1):9818.

One of the main problems that a brain-computer interface (BCI) face is that a training stage is required for acquiring training data to calibrate its classification model just before every use. Transfer learning is a promising method for addressing the problem. In this paper, we propose a Riemannian geometry-based transfer learning algorithm for code modulated visual evoked potential (c-VEP)-based BCIs, which can effectively reduce the calibration time without sacrificing the classification accuracy. The algorithm includes the main procedures of log-Euclidean data alignment (LEDA), super-trial construction, covariance matrix estimation, training accuracy-based subject selection (TSS) and minimum distance to mean classification. Among them, the LEDA reduces the difference in data distribution between subjects, whereas the TSS promotes the similarity between a target subject and the source subjects. The resulting performance of transfer learning is improved significantly. Sixteen subjects participated in a c-VEP BCI experiment and the recorded data were used in offline analysis. Leave-one subject-out (LOSO) cross-validation was used to evaluate the proposed algorithm on the data set. The results showed that the algorithm achieved much higher classification accuracy than the subject-specific (baseline) algorithm with the same number of training trials. Equivalently, the algorithm reduces the training time of the BCI at the same performance level and thus facilitates its application in real world.

RevDate: 2022-06-14

Zhao R, Yang Z, Zheng H, et al (2022)

A framework for the general design and computation of hybrid neural networks.

Nature communications, 13(1):3427.

There is a growing trend to design hybrid neural networks (HNNs) by combining spiking neural networks and artificial neural networks to leverage the strengths of both. Here, we propose a framework for general design and computation of HNNs by introducing hybrid units (HUs) as a linkage interface. The framework not only integrates key features of these computing paradigms but also decouples them to improve flexibility and efficiency. HUs are designable and learnable to promote transmission and modulation of hybrid information flows in HNNs. Through three cases, we demonstrate that the framework can facilitate hybrid model design. The hybrid sensing network implements multi-pathway sensing, achieving high tracking accuracy and energy efficiency. The hybrid modulation network implements hierarchical information abstraction, enabling meta-continual learning of multiple tasks. The hybrid reasoning network performs multimodal reasoning in an interpretable, robust and parallel manner. This study advances cross-paradigm modeling for a broad range of intelligent tasks.

RevDate: 2022-06-14

Qi Y, Zhu X, Xu K, et al (2022)

Dynamic Ensemble Bayesian Filter for Robust Control of a Human Brain-Machine Interface.

IEEE transactions on bio-medical engineering, PP: [Epub ahead of print].

OBJECTIVE: Brain-machine interfaces (BMIs) aim to provide direct brain control of devices such as prostheses and computer cursors, which have demonstrated great potential for motor restoration. One major limitation of current BMIs lies in the unstable performance due to the variability of neural signals, especially in online control, which seriously hinders the clinical availability of BMIs.

METHOD: We propose a dynamic ensemble Bayesian filter (DyEnsemble) to deal with the neural variability in online BMI control. Unlike most existing approaches using fixed models, DyEnsemble learns a pool of models that contains diverse abilities in describing the neural functions. In each time slot, it dynamically weights and assembles the models according to the neural signals in a Bayesian framework. In this way, DyEnsemble copes with variability in signals and improves the robustness of online control.

RESULTS: Online BMI experiments with a human participant demonstrate that, compared with the velocity Kalman filter, DyEnsemble significantly improves the control accuracy (increases the success rate by 13.9% in the random target pursuit task) and robustness (performs more stably over different experiment days).

CONCLUSION: Experimental results demonstrate the superiority of DyEnsemble in online BMI control.

SIGNIFICANCE: DyEnsemble frames a novel and flexible dynamic decoding framework for robust BMIs, beneficial to various neural decoding applications.

RevDate: 2022-06-14

Yang E, Hou W, Liu K, et al (2022)

A multifunctional chitosan hydrogel dressing for liver hemostasis and infected wound healing.

Carbohydrate polymers, 291:119631.

For the treatment of infected bleeding wounds, we compounded methacrylate anhydride dopamine (DAMA) and Zn-doped whitlockite nanoparticles (Zn-nWH) into methacrylate anhydride quaternized chitosan (QCSMA) to obtain a multifunctional hydrogel dressing (QCSMA/DAMA/Zn-nWH) with hemostasis, disinfection and wound healing promotion. QCSMA/DAMA/Zn-nWH exhibited good adhesion (0.031 MPa) and DPPH scavenging ability (94%), favorable biocompatibility (hemolysis ratio < 2%, no cytotoxicity), and showed a low BCI value (< 13%) in vitro coagulation test and could activate coagulation pathway. In addition, QCSMA/DAMA/Zn-nWH had excellent hemostatic effect (129 ± 22 s, 27 ± 5 mg) in vivo compared with the control (571 ± 15 s, 147 ± 31 mg) and CCS (354 ± 27 s, 110 ± 46 mg). Meanwhile, QCSMA/DAMA/Zn-nWH showed excellent antibacterial properties (> 90% against S. aureus and E. coli) and could promote collagen deposition, reduce inflammatory expression and promote wound healing. All results indicate that these multifunctional hydrogel dressings have great potential in clinical hemostasis and anti-infection healing.

RevDate: 2022-06-13

Renton AI, Painter DR, JB Mattingley (2022)

Optimising the classification of feature-based attention in frequency-tagged electroencephalography data.

Scientific data, 9(1):296.

Brain-computer interfaces (BCIs) are a rapidly expanding field of study and require accurate and reliable real-time decoding of patterns of neural activity. These protocols often exploit selective attention, a neural mechanism that prioritises the sensory processing of task-relevant stimulus features (feature-based attention) or task-relevant spatial locations (spatial attention). Within the visual modality, attentional modulation of neural responses to different inputs is well indexed by steady-state visual evoked potentials (SSVEPs). These signals are reliably present in single-trial electroencephalography (EEG) data, are largely resilient to common EEG artifacts, and allow separation of neural responses to numerous concurrently presented visual stimuli. To date, efforts to use single-trial SSVEPs to classify visual attention for BCI control have largely focused on spatial attention rather than feature-based attention. Here, we present a dataset that allows for the development and benchmarking of algorithms to classify feature-based attention using single-trial EEG data. The dataset includes EEG and behavioural responses from 30 healthy human participants who performed a feature-based motion discrimination task on frequency tagged visual stimuli.

RevDate: 2022-06-13

Dingle AM, Moxon K, Shokur S, et al (2022)

Editorial: Getting Neuroprosthetics Out of the Lab: Improving the Human-Machine Interactions to Restore Sensory-Motor Functions.

Frontiers in robotics and AI, 9:928383 pii:928383.

RevDate: 2022-06-13

Mashrur FR, Rahman KM, Miya MTI, et al (2022)

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework.

Frontiers in human neuroscience, 16:861270.

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

RevDate: 2022-06-13

Ortega-Martinez A, Von Lühmann A, Farzam P, et al (2022)

Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.

Neurophotonics, 9(2):025003.

Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain-computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal. Aim: We propose an implementation of the Kalman filter with time-embedded canonical correlation analysis for the real-time regression of fNIRS signals with multivariate nuisance regressors that take multiple delays into consideration. Approach: We tested our proposed method on a previously acquired finger tapping dataset with the purpose of classifying the neural responses as left or right. Results: We demonstrate computationally efficient real-time processing of 24-channel fNIRS data (400 samples per second per channel) with a two order of selective magnitude decrease in cardiac signal power and up to sixfold increase in the contrast-to-noise ratio compared with the nonregressed signals. Conclusion: The method provides a way to obtain better distinction of brain from non-brain signals in real time for BCI application with fNIRS.

RevDate: 2022-06-13

Ma T, Huggins JE, J Kang (2021)

Adaptive Sequence-Based Stimulus Selection in an ERP-Based Brain-Computer Interface by Thompson Sampling in a Multi-Armed Bandit Problem.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2021:3648-3655.

A Brain-Computer Interface (BCI) is a device that interprets brain activity to help people with disabilities communicate. The P300 ERP-based BCI speller displays a series of events on the screen and searches the elicited electroencephalogram (EEG) data for target P300 event-related potential (ERP) responses among a series of non-target events. The Checkerboard (CB) paradigm is a common stimulus presentation paradigm. Although a few studies have proposed data-driven methods for stimulus selection, they suffer from intractable decision rules, large computation complexity, or error propagation for participants who perform poorly under the static paradigm. In addition, none of the methods have been applied to the CB paradigm directly. In this work, we propose a sequence-based adaptive stimulus selection method using Thompson Sampling in the multi-bandit problem with multiple actions. During each sequence, the algorithm selects a random subset of stimuli with fixed size, aiming to identify all target stimuli and to improve the spelling speed by reducing the number of unnecessary non-target stimuli. We compute "clean" stimulus-specific rewards from raw classifier scores via the Bayes rule. We perform extensive simulation studies to compare our algorithm to the static CB paradigm. We show the robustness of our algorithm by considering the constraints of practical use. For scenarios where simulated data resemble the real data the most, the spelling efficiency of our algorithm increases by more than 70%, compared to the static CB paradigm.

RevDate: 2022-06-13

Perry Fordson H, Xing X, Guo K, et al (2022)

Not All Electrode Channels Are Needed: Knowledge Transfer From Only Stimulated Brain Regions for EEG Emotion Recognition.

Frontiers in neuroscience, 16:865201.

Emotion recognition from affective brain-computer interfaces (aBCI) has garnered a lot of attention in human-computer interactions. Electroencephalographic (EEG) signals collected and stored in one database have been mostly used due to their ability to detect brain activities in real time and their reliability. Nevertheless, large EEG individual differences occur amongst subjects making it impossible for models to share information across. New labeled data is collected and trained separately for new subjects which costs a lot of time. Also, during EEG data collection across databases, different stimulation is introduced to subjects. Audio-visual stimulation (AVS) is commonly used in studying the emotional responses of subjects. In this article, we propose a brain region aware domain adaptation (BRADA) algorithm to treat features from auditory and visual brain regions differently, which effectively tackle subject-to-subject variations and mitigate distribution mismatch across databases. BRADA is a new framework that works with the existing transfer learning method. We apply BRADA to both cross-subject and cross-database settings. The experimental results indicate that our proposed transfer learning method can improve valence-arousal emotion recognition tasks.

RevDate: 2022-06-11

Pitt KM, Mansouri A, Wang Y, et al (2022)

Toward P300-brain-computer interface access to contextual scene displays for AAC: An initial exploration of context and asymmetry processing in healthy adults.

Neuropsychologia pii:S0028-3932(22)00148-8 [Epub ahead of print].

Brain-computer interfaces for augmentative and alternative communication (BCI-AAC) may help overcome physical barriers to AAC access. Traditionally, visually based P300-BCI-AAC displays utilize a symmetrical grid layout. Contextual scene displays are composed of context-rich images (e.g., photographs) and may support AAC success. However, contextual scene displays contrast starkly with the standard P300-grid approach. Understanding the neurological processes from which BCI-AAC devices function is crucial to human-centered computing for BCI-AAC. Therefore, the aim of this multidisciplinary investigation is to provide an initial exploration of contextual scene use for BCI-AAC.

METHODS: Participants completed three experimental conditions to evaluate the effects of item arrangement asymmetry and context on P300-based BCI-AAC signals and offline BCI-AAC accuracy, including 1) the full contextual scene condition, 2) asymmetrical item arraignment without context condition and 3) the grid condition. Following each condition, participants completed task-evaluation ratings (e.g., engagement). Offline BCI-AAC accuracy for each condition was evaluated using cross-validation.

RESULTS: Display asymmetry significantly decreased P300 latency in the centro-parietal cluster. P300 amplitudes in the frontal cluster were decreased, though nonsignificantly. Display context significantly increased N170 amplitudes in the occipital cluster, and N400 amplitudes in the centro-parietal and occipital clusters. Scenes were rated as more visually appealing and engaging, and offline BCI-AAC performance for the scene condition was not statistically different from the grid standard.

CONCLUSION: Findings support the feasibility of incorporating scene-based displays for P300-BCI-AAC development to help provide communication for individuals with minimal or emerging language and literacy skills.

RevDate: 2022-06-10

Liu D, Xu X, Li D, et al (2022)

Intracranial brain-computer interface spelling using localized visual motion response.

NeuroImage pii:S1053-8119(22)00482-7 [Epub ahead of print].

Intracranial brain-computer interfaces (BCIs) can assist severely disabled persons in text communication and environmental control with high precision and speed. Nevertheless, sustainable BCI implants require minimal invasiveness. One of the implantation strategies is to adopt localized and robust cortical activities to drive BCI communication and to make a precise presurgical planning. The visual motion response is a good candidate for inclusion in this strategy because of its focal activity over the middle temporal visual area (MT). Here, we developed an intracranial BCI for spelling, utilizing only three electrodes over the MT area. The best recording electrodes were decided by preoperative functional magnetic resonance imaging (MRI) localization of the MT, and local neural activities were further enhanced by differential rereferencing of these electrodes. The BCI spelling system was validated both offline and online by five epilepsy patients, achieving the fastest speed of 62 bits/min, i.e., 12 characters/min. Moreover, the response patterns of dual-directional visual motion stimuli provided an additional dimension of BCI target encoding and paved the way for a higher information transfer rate of intracranial BCI spelling.

RevDate: 2022-06-10

Niu J, N Jiang (2022)

Pseudo-online detection and classification for upper-limb movements.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: This study analyzed detection (movement vs. non-movement) and classification (different types of movements) to decode upper-limb movement volitions in a pseudo-online fashion.

APPROACH: Nine healthy subjects executed four self-initiated movements: left wrist extension, right wrist extension, left index finger extension, and right index finger extension. For detection, we investigated the performance of three individual classifiers (SVM, EEGNET, and Riemannian geometry featured SVM) on three frequency bands (0.05-5Hz, 5-40Hz, 0.05-40Hz). The best frequency band and the best classifier combinations were constructed to realize an ensemble processing pipeline using majority voting. For classification, we used adaptive boosted Riemannian geometry model to differentiate contra-lateral and ipsilateral movements Main results: The ensemble model achieved 79.6 ± 8.8% true positive rate and 3.1 ± 1.2 false positives per minute with 75.3 ± 112.6ms latency on a pseudo-online detection task. The following classification gave around 67% accuracy to differentiate contralateral movements.

SIGNIFICANCE: The newly proposed ensemble method and pseudo-online testing procedure could provide a robust BCI design for movement decoding.

RevDate: 2022-06-10

Tian P, Xu G, Han C, et al (2022)

Effects of Paradigm Color and Screen Brightness on Visual Fatigue in Light Environment of Night Based on Eye Tracker and EEG Acquisition Equipment.

Sensors (Basel, Switzerland), 22(11): pii:s22114082.

Nowadays, more people tend to go to bed late and spend their sleep time with various electronic devices. At the same time, the BCI (brain-computer interface) rehabilitation equipment uses a visual display, thus it is necessary to evaluate the problem of visual fatigue to avoid the impact on the training effect. Therefore, it is very important to understand the impact of using electronic devices in a dark environment at night on human visual fatigue. This paper uses Matlab to write different color paradigm stimulations, uses a 4K display with an adjustable screen brightness to jointly design the experiment, uses eye tracker and g.tec Electroencephalogram (EEG) equipment to collect the signal, and then carries out data processing and analysis, finally obtaining the influence of the combination of different colors and different screen brightness on human visual fatigue in a dark environment. In this study, subjects were asked to evaluate their subjective (Likert scale) perception, and objective signals (pupil diameter, θ + α frequency band data) were collected in a dark environment (<3 lx). The Likert scale showed that a low screen brightness in the dark environment could reduce the visual fatigue of the subjects, and participants preferred blue to red. The pupil data revealed that visual perception sensitivity was more vulnerable to stimulation at a medium and high screen brightness, which is easier to deepen visual fatigue. EEG frequency band data concluded that there was no significant difference between paradigm colors and screen brightness on visual fatigue. On this basis, this paper puts forward a new index-the visual anti-fatigue index, which provides a valuable reference for the optimization of the indoor living environment, the improvement of satisfaction with the use of electronic equipment and BCI rehabilitation equipment, and the protection of human eyes.

RevDate: 2022-06-10

Varandas R, Lima R, Bermúdez I Badia S, et al (2022)

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning.

Sensors (Basel, Switzerland), 22(11): pii:s22114010.

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain-Computer Interfaces (BCI) allows for unobtrusively monitoring one's cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human-computer interaction variables.

RevDate: 2022-06-08

Kim MG, Lim H, Lee HS, et al (2022)

Brain-computer interface-based action observation combined with peripheral electrical stimulation enhances corticospinal excitability in healthy subjects and stroke patients.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Action observation (AO) combined with brain computer interface (BCI) technology enhances the cortical activation. Peripheral electrical stimulation (PES) is known to increase the corticospinal excitability, thereby activating brain plasticity. To maximize motor recovery, we assessed the effects of BCI-AO combined with PES on corticospinal plasticity.

APPROACH: Seventeen patients with chronic hemiplegic stroke and 17 healthy subjects were recruited. The participants watched a video of repetitive grasping actions with four different tasks for 15 mins: A) AO alone; B) AO + PES; C) BCI-AO + continuous PES; D) BCI-AO + triggered PES. PES was applied at the ulnar nerve of the wrist. The tasks were performed in a random order at least 3 days apart. We assessed the latency and amplitude of the motor evoked potentials (MEPs). We examined changes in MEP parameters pre-and post-exercise across the four tasks in the FDI muscle of the dominant hand (healthy subjects) and affected hand (stroke patients).

MAIN RESULTS: The decrease in MEP latency and increase in MEP amplitude after the four tasks were significant in both groups. The increase in MEP amplitude was sustained for 20 mins after tasks B, C, and D in both groups. The increase in MEP amplitude was significant between tasks A vs B, B vs C, and C vs D. The estimated mean difference in MEP amplitude post-exercise was highest for A and D in both groups.

SIGNIFICANCE: The results indicate that BCI-AO combined with PES is superior to AO alone or AO + PES for facilitating corticospinal plasticity in both healthy subjects and stroke patients. Furthermore, this study supports the idea that synchronized activation of cortical and peripheral networks can enhance neuroplasticity after stroke. We suggest that the BCI-AO paradigm and PES could provide a novel neurorehabilitation strategy for stroke patients.

RevDate: 2022-06-07

Hughes CL, Flesher SN, RA Gaunt (2022)

Effects of stimulus pulse rate on somatosensory adaptation in the human cortex.

Brain stimulation pii:S1935-861X(22)00100-0 [Epub ahead of print].

BACKGROUND: Intracortical microstimulation (ICMS) of the somatosensory cortex can restore sensation to people with neurological diseases. However, many aspects of ICMS are poorly understood, including the effect of stimulation on percept intensity over time.

OBJECTIVE: Here, we evaluate how tactile percepts, evoked by ICMS in the somatosensory cortex of a human participant adapt over time.

METHODS: We delivered continuous and intermittent ICMS to the somatosensory cortex and assessed the reported intensity of tactile percepts over time in a human participant. Experiments were conducted over approximately one year and linear mixed effects models were used to assess significance.

RESULTS: Continuous stimulation at high frequencies led to rapid decreases in intensity, while low frequency stimulation maintained percept intensity for longer periods. Burst-modulated stimulation extended the time before the intensity began to decrease, but all protocols ultimately resulted in complete sensation loss within one minute. Intermittent stimulation paradigms with several seconds between stimulus trains evoked intermittent percepts and also led to decreases in intensity on many electrodes, but never resulted in extinction of the sensation after over three minutes of stimulation. Longer breaks between each pulse train resulted in some recovery in the intensity of the stimulus-evoked percepts. For several electrodes, intermittent stimulation had almost no effect on the perceived intensity.

CONCLUSIONS: Intermittent ICMS paradigms were more effective at maintaining percepts. Given that transient neural activity dominates the response in somatosensory cortex during mechanical contact onsets and offsets, providing brief stimulation trains at these times may more closely represent natural cortical activity and have the additional benefit of prolonging the ability to evoke sensations over longer time periods.

RevDate: 2022-06-07

Robinson JT, Rommelfanger KS, Anikeeva PO, et al (2022)

Building a culture of responsible neurotech: Neuroethics as socio-technical challenges.

Neuron pii:S0896-6273(22)00413-5 [Epub ahead of print].

Scientists around the globe are joining the race to achieve engineering feats to read, write, modulate, and interface with the human brain in a broadening continuum of invasive to non-invasive ways. The expansive implications of neurotechnology for our conception of health, mind, decision-making, and behavior has raised social and ethical considerations that are inextricable from neurotechnological progress. We propose "socio-technical" challenges as a framing to integrate neuroethics into the engineering process. Intentionally aligning societal and engineering goals within this framework offers a way to maximize the positive impact of next-generation neurotechnologies on society.

RevDate: 2022-06-07

Doya K, Ema A, Kitano H, et al (2022)

Social impact and governance of AI and neurotechnologies.

Neural networks : the official journal of the International Neural Network Society, 152:542-554 pii:S0893-6080(22)00186-1 [Epub ahead of print].

Advances in artificial intelligence (AI) and brain science are going to have a huge impact on society. While technologies based on those advances can provide enormous social benefits, adoption of new technologies poses various risks. This article first reviews the co-evolution of AI and brain science and the benefits of brain-inspired AI in sustainability, healthcare, and scientific discoveries. We then consider possible risks from those technologies, including intentional abuse, autonomous weapons, cognitive enhancement by brain-computer interfaces, insidious effects of social media, inequity, and enfeeblement. We also discuss practical ways to bring ethical principles into practice. One proposal is to stop giving explicit goals to AI agents and to enable them to keep learning human preferences. Another is to learn from democratic mechanisms that evolved in human society to avoid over-consolidation of power. Finally, we emphasize the importance of open discussions not only by experts, but also including a diverse array of lay opinions.

RevDate: 2022-06-07

Fujiwara Y, J Ushiba (2022)

Deep Residual Convolutional Neural Networks for Brain-Computer Interface to Visualize Neural Processing of Hand Movements in the Human Brain.

Frontiers in computational neuroscience, 16:882290.

Concomitant with the development of deep learning, brain-computer interface (BCI) decoding technology has been rapidly evolving. Convolutional neural networks (CNNs), which are generally used as electroencephalography (EEG) classification models, are often deployed in BCI prototypes to improve the estimation accuracy of a participant's brain activity. However, because most BCI models are trained, validated, and tested via within-subject cross-validation and there is no corresponding generalization model, their applicability to unknown participants is not guaranteed. In this study, to facilitate the generalization of BCI model performance to unknown participants, we trained a model comprising multiple layers of residual CNNs and visualized the reasons for BCI classification to reveal the location and timing of neural activities that contribute to classification. Specifically, to develop a BCI that can distinguish between rest, left-hand movement, and right-hand movement tasks with high accuracy, we created multilayers of CNNs, inserted residual networks into the multilayers, and used a larger dataset than in previous studies. The constructed model was analyzed with gradient-class activation mapping (Grad-CAM). We evaluated the developed model via subject cross-validation and found that it achieved significantly improved accuracy (85.69 ± 1.10%) compared with conventional models or without residual networks. Grad-CAM analysis of the classification of cases in which our model produced correct answers showed localized activity near the premotor cortex. These results confirm the effectiveness of inserting residual networks into CNNs for tuning BCI. Further, they suggest that recording EEG signals over the premotor cortex and some other areas contributes to high classification accuracy.

RevDate: 2022-06-07

Liu Y, Höllerer T, M Sra (2022)

SRI-EEG: State-Based Recurrent Imputation for EEG Artifact Correction.

Frontiers in computational neuroscience, 16:803384.

Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. In this work, we address the EEG noise/artifact correction problem. Our goal is to detect physiological artifacts in EEG signal and automatically replace the detected artifacts with imputed values to enable robust EEG sensing overall requiring significantly reduced manual effort than is usual. We present a novel EEG state-based imputation model built upon a recurrent neural network, which we call SRI-EEG, and evaluate the proposed method on three publicly available EEG datasets. From quantitative and qualitative comparisons with six conventional and neural network based approaches, we demonstrate that our method achieves comparable performance to the state-of-the-art methods on the EEG artifact correction task.

RevDate: 2022-06-07

Pais-Vieira C, Gaspar P, Matos D, et al (2022)

Embodiment Comfort Levels During Motor Imagery Training Combined With Immersive Virtual Reality in a Spinal Cord Injury Patient.

Frontiers in human neuroscience, 16:909112.

Brain-machine interfaces combining visual, auditory, and tactile feedback have been previously used to generate embodiment experiences during spinal cord injury (SCI) rehabilitation. It is not known if adding temperature to these modalities can result in discomfort with embodiment experiences. Here, comfort levels with the embodiment experiences were investigated in an intervention that required a chronic pain SCI patient to generate lower limb motor imagery commands in an immersive environment combining visual (virtual reality -VR), auditory, tactile, and thermal feedback. Assessments were made pre-/ post-, throughout the intervention (Weeks 0-5), and at 7 weeks follow up. Overall, high levels of embodiment in the adapted three-domain scale of embodiment were found throughout the sessions. No significant adverse effects of VR were reported. Although sessions induced only a modest reduction in pain levels, an overall reduction occurred in all pain scales (Faces, Intensity, and Verbal) at follow up. A high degree of comfort in the comfort scale for the thermal-tactile sleeve, in both the thermal and tactile feedback components of the sleeve was reported. This study supports the feasibility of combining multimodal stimulation involving visual (VR), auditory, tactile, and thermal feedback to generate embodiment experiences in neurorehabilitation programs.

RevDate: 2022-06-06

Israsena P, S Pan-Ngum (2022)

A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG.

Frontiers in computational neuroscience, 16:868642.

This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To make use of SSVEP measured around the ears following the ear-EEG concept, especially for practical binaural implementation, we propose a CNN structure coupled with regressed softmax outputs to improve accuracy. Evaluating on a public dataset, we studied classification performance for both subject-dependent and subject-independent trainings. It was found that with the proposed structure using a group training approach, a 69.21% accuracy was achievable. An ITR of 6.42 bit/min given 63.49 % accuracy was recorded while only monitoring data from T7 and T8. This represents a 12.47% improvement from a single ear implementation and illustrates potential of the approach to enhance performance for practical implementation of wearable EEG.

RevDate: 2022-06-06

Oralhan Z, Oralhan B, Khayyat MM, et al (2022)

3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User.

Computational and mathematical methods in medicine, 2022:8452002.

This research was aimed at presenting performance of 3-dimensional input convolutional neural networks for steady-state visual evoked potential classification in a wireless EEG-based brain-computer interface system. Overall performance of a brain-computer interface system depends on information transfer rate. Parameters such as signal classification accuracy rate, signal stimulator structure, and user task completion time affect information transfer rate. In this study, we used 3 types of signal classification methods that are 1-dimensional, 2-dimensional, and 3-dimensional input convolutional neural network. According to online experiment with using 3-dimensional input convolutional neural network, we reached average classification accuracy rate and average information transfer rate as 93.75% and 58.35 bit/min, respectively. This both results significantly higher than the other methods that we used in experiments. Moreover, user task completion time was reduced with using 3-dimensional input convolutional neural network. Our proposed method is novel and state-of-art model for steady-state visual evoked potential classification.

RevDate: 2022-06-06

Ahn M, Jun SC, Yeom HG, et al (2022)

Editorial: Deep Learning in Brain-Computer Interface.

Frontiers in human neuroscience, 16:927567.

RevDate: 2022-06-07

Wang X, Weltman Hirschberg A, Xu H, et al (2020)

A Parylene Neural Probe Array for Multi-Region Deep Brain Recordings.

Journal of microelectromechanical systems : a joint IEEE and ASME publication on microstructures, microactuators, microsensors, and microsystems, 29(4):499-513.

A Parylene C polymer neural probe array with 64 electrodes purposefully positioned across 8 individual shanks to anatomically match specific regions of the hippocampus was designed, fabricated, characterized, and implemented in vivo for enabling recording in deep brain regions in freely moving rats. Thin film polymer arrays were fabricated using surface micromachining techniques and mechanically braced to prevent buckling during surgical implantation. Importantly, the mechanical bracing technique developed in this work involves a novel biodegradable polymer brace that temporarily reduces shank length and consequently, increases its stiffness during implantation, therefore enabling access to deeper brain regions while preserving a low original cross-sectional area of the shanks. The resulting mechanical properties of braced shanks were evaluated at the benchtop. Arrays were then implemented in vivo in freely moving rats, achieving both acute and chronic recordings from the pyramidal cells in the cornu ammonis (CA) 1 and CA3 regions of the hippocampus which are responsible for memory encoding. This work demonstrated the potential for minimally invasive polymer-based neural probe arrays for multi-region recording in deep brain structures.

RevDate: 2022-06-07

Feldotto B, Eppler JM, Jimenez-Romero C, et al (2022)

Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure.

Frontiers in neuroinformatics, 16:884180.

Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.

RevDate: 2022-06-07

Zhang M, Li C, Liu SY, et al (2022)

An electroencephalography-based human-machine interface combined with contralateral C7 transfer in the treatment of brachial plexus injury.

Neural regeneration research, 17(12):2600-2605.

Transferring the contralateral C7 nerve root to the median or radial nerve has become an important means of repairing brachial plexus nerve injury. However, outcomes have been disappointing. Electroencephalography (EEG)-based human-machine interfaces have achieved promising results in promoting neurological recovery by controlling a distal exoskeleton to perform functional limb exercises early after nerve injury, which maintains target muscle activity and promotes the neurological rehabilitation effect. This review summarizes the progress of research in EEG-based human-machine interface combined with contralateral C7 transfer repair of brachial plexus nerve injury. Nerve transfer may result in loss of nerve function in the donor area, so only nerves with minimal impact on the donor area, such as the C7 nerve, should be selected as the donor. Single tendon transfer does not fully restore optimal joint function, so multiple functions often need to be reestablished simultaneously. Compared with traditional manual rehabilitation, EEG-based human-machine interfaces have the potential to maximize patient initiative and promote nerve regeneration and cortical remodeling, which facilitates neurological recovery. In the early stages of brachial plexus injury treatment, the use of an EEG-based human-machine interface combined with contralateral C7 transfer can facilitate postoperative neurological recovery by making full use of the brain's computational capabilities and actively controlling functional exercise with the aid of external machinery. It can also prevent disuse atrophy of muscles and target organs and maintain neuromuscular junction effectiveness. Promoting cortical remodeling is also particularly important for neurological recovery after contralateral C7 transfer. Future studies are needed to investigate the mechanism by which early movement delays neuromuscular junction damage and promotes cortical remodeling. Understanding this mechanism should help guide the development of neurological rehabilitation strategies for patients with brachial plexus injury.

RevDate: 2022-06-07

Davis KC, Meschede-Krasa B, Cajigas I, et al (2022)

Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury.

Journal of neuroengineering and rehabilitation, 19(1):53.

OBJECTIVE: The objective of this study was to develop a portable and modular brain-computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A).

BACKGROUND: BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home.

METHODS: The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject's wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use.

RESULTS: Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject's caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining.

CONCLUSIONS: The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015.

RevDate: 2022-06-06

Teng Y, Sun Y, Chen X, et al (2022)

Research on Effective Recognition of Alarm Signals in Human-Machine System Based on Cognitive Neural Experiments.

International journal of occupational safety and ergonomics : JOSE [Epub ahead of print].

The reasonable design of the alarm signal in the man-machine system is one of the important factors that determine the occurrence of safety accidents. Neuroergonomics provides a new perspective for the study of the cognitive process of alarm signals, which can reveal the mechanism of human perception of visual alarm signals from the cognitive level of the brain, thereby identifying the effectiveness of alarm signals. The article's research simulated the human-machine system for heat dissipation of new energy vehicles, used the automatic control interface of the cooling water system as the stimulus material, and used the event-related potential technology in cognitive neuroscience for experimental verification. The experimental results showed that: three kinds of alarm signals (color, color + shape, color + orientation) all induce visual mismatch waves, and the effective response of human to the alarm signal is color + orientation, color + shape, color from small to large, which provides a reference for the design of the alarm signal of the man-machine system.

RevDate: 2022-06-07

Kim HJ, JS Ho (2022)

Wireless interfaces for brain neurotechnologies.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 380(2228):20210020.

Wireless interfaces enable brain-implanted devices to remotely interact with the external world. They are critical components in modern research and clinical neurotechnologies and play a central role in determining their overall size, lifetime and functionality. Wireless interfaces use a wide range of modalities-including radio-frequency fields, acoustic waves and light-to transfer energy and data to and from an implanted device. These forms of energy interact with living tissue through distinct mechanisms and therefore lead to systems with vastly different form factors, operating characteristics, and safety considerations. This paper reviews recent advances in the development of wireless interfaces for brain neurotechnologies. We summarize the requirements that state-of-the-art brain-implanted devices impose on the wireless interface, and discuss the working principles and applications of wireless interfaces based on each modality. We also investigate challenges associated with wireless brain neurotechnologies and discuss emerging solutions permitted by recent developments in electrical engineering and materials science. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

RevDate: 2022-06-07

Huang X, Liang S, Li Z, et al (2022)

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review.

PloS one, 17(6):e0269001 pii:PONE-D-21-13885.

Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.

RevDate: 2022-06-03

Chi X, Wan C, Wang C, et al (2022)

A Novel Hybrid Brain-Computer Interface Combining Motor Imagery and Intermodulation Steady-State Visual Evoked Potential.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

The hybrid brain-computer interface (hBCI) combining motor imagery (MI) and steady-state visual evoked potential (SSVEP) has been proven to have better performance than a pure MI- or SSVEP-based brain-computer interface (BCI). In most studies on hBCIs, subjects have been required to focus their attention on flickering light-emitting diodes (LEDs) or blocks while imagining body movements. However, these two classical tasks performed concurrently have a poor correlation. Therefore, it is necessary to reduce the task complexity of such a system and improve its user-friendliness. Aiming to achieve this goal, this study proposes a novel hybrid BCI that combines MI and intermodulation SSVEPs. In the proposed system, images of both hands flicker at the same frequency (i.e., 30 Hz) but at different grasp frequencies (i.e., 1 Hz for the left hand, and 1.5 Hz for the right hand), resulting in different intermodulation frequencies for encoding targets. Additionally, movement observation for subjects can help to perform the MI task better. In this study, two types of brain signals are classified independently and then fused by a scoring mechanism based on the probability distribution of relevant parameters. The online verification results showed that the average accuracies of 12 healthy subjects and 11 stroke patients were 92.40 ± 7.45% and 73.07 ± 9.07%, respectively. The average accuracies of 10 healthy subjects in the MI, SSVEP, and hybrid tasks were 84.00 ± 12.81%, 80.75 ± 8.08%, and 89.00 ± 9.94%, respectively. The high recognition accuracy verifies the feasibility and robustness of the proposed system. This study provides a novel and natural paradigm for a hybrid BCI based on MI and SSVEP.

RevDate: 2022-06-06

Goldway N, Jalon I, Keynan JN, et al (2022)

Feasibility and utility of amygdala neurofeedback.

Neuroscience and biobehavioral reviews, 138:104694 pii:S0149-7634(22)00183-X [Epub ahead of print].

Amygdala NeuroFeedback (NF) have the potential of being a valuable non-invasive intervention tool in many psychiatric disporders. However, the feasibility and best practices of this method have not been systematically examined. The current article presents a review of amygdala-NF studies, an analytic summary of study design parameters, and examination of brain mechanisms related to successful amygdala-NF performance. A meta-analysis of 33 publications showed that real amygdala-NF facilitates learned modulation compared to control conditions. In addition, while variability in study dsign parameters is high, these design choices are implicitly organized by the targeted valence domain (positive or negative). However, in most cases the neuro-behavioral effects of targeting such domains were not directly assessed. Lastly, re-analyzing six data sets of amygdala-fMRI-NF revealed that successful amygdala down-modulation is coupled with deactivation of the posterior insula and nodes in the Default-Mode-Network. Our findings suggest that amygdala self-modulation can be acquired using NF. Yet, additional controlled studies, relevant behavioral tasks before and after NF intervention, and neural 'target engagement' measures are critically needed to establish efficacy and specificity. In addition, the fMRI analysis presented here suggest that common accounts regarding the brain network involved in amygdala NF might reflect unsuccessful modulation attempts rather than successful modulation.

RevDate: 2022-06-02

Edmondson LR, HP Saal (2022)

Getting a grasp on BMIs: Decoding prehension and speech signals.

Neuron, 110(11):1743-1745.

Wandelt et al. (2022) show that different grasps can be decoded from neural activity in the human supramarginal gyrus (SMG), ventral premotor cortex, and somatosensory cortex during motor imagery and speech, highlighting the attractiveness of higher-level areas such as the SMG for brain-machine interface applications.

RevDate: 2022-06-02

Enz N, Schmidt J, Nolan K, et al (2022)

Self-regulation of the brain's right frontal Beta rhythm using a brain-computer interface.

Psychophysiology [Epub ahead of print].

Neural oscillations, or brain rhythms, fluctuate in a manner reflecting ongoing behavior. Whether these fluctuations are instrumental or epiphenomenal to the behavior remains elusive. Attempts to experimentally manipulate neural oscillations exogenously using noninvasive brain stimulation have shown some promise, but difficulty with tailoring stimulation parameters to individuals has hindered progress in this field. We demonstrate here using electroencephalography (EEG) neurofeedback in a brain-computer interface that human participants (n = 44) learned over multiple sessions across a 6-day period to self-regulate their Beta rhythm (13-20 Hz), either up or down, over the right inferior frontal cortex. Training to downregulate Beta was more effective than training to upregulate Beta. The modulation was evident only during neurofeedback task performance but did not lead to offline alteration of Beta rhythm characteristics at rest, nor to changes in subsequent cognitive behavior. Likewise, a control group (n = 38) who underwent training to up or downregulate the Alpha rhythm (8-12 Hz) did not exhibit behavioral changes. Although the right frontal Beta rhythm has been repeatedly implicated as a key component of the brain's inhibitory control system, the present data suggest that its manipulation offline prior to cognitive task performance does not result in behavioral change in healthy individuals. Whether this form of neurofeedback training could serve as a useful therapeutic target for disorders with dysfunctional inhibitory control as their basis remains to be tested in a context where performance is abnormally poor and neural dynamics are different.

RevDate: 2022-06-01

Christie B, Osborn LE, McMullen DP, et al (2022)

Perceived timing of cutaneous vibration and intracortical microstimulation of human somatosensory cortex.

Brain stimulation pii:S1935-861X(22)00094-8 [Epub ahead of print].

BACKGROUND: Intracortical microstimulation (ICMS) of somatosensory cortex can partially restore the sense of touch. Though ICMS bypasses much of the neuraxis, prior studies have found that conscious detection of touch elicited by ICMS lags behind the detection of cutaneous vibration. These findings may have been influenced by mismatched stimulus intensities, which can impact temporal perception.

OBJECTIVE: Evaluate the relative latency at which intensity-matched vibration and ICMS are perceived by a human participant.

METHODS: One person implanted with microelectrode arrays in somatosensory cortex performed reaction time and temporal order judgment (TOJ) tasks. To measure reaction time, the participant reported when he perceived vibration or ICMS. In the TOJ task, vibration and ICMS were sequentially presented and the participant reported which stimulus occurred first. To verify that the participant could distinguish between stimuli, he also performed a modality discrimination task, in which he indicated if he felt vibration, ICMS, or both.

RESULTS: When vibration was matched in perceived intensity to high-amplitude ICMS, vibration was perceived, on average, 48 ms faster than ICMS. However, in the TOJ task, both sensations arose at comparable latencies, with points of subjective simultaneity not significantly different from zero. The participant could discriminate between tactile modalities above chance level but was more inclined to report feeling vibration than ICMS.

CONCLUSIONS: The latencies of ICMS-evoked percepts are slower than their mechanical counterparts. However, differences in latencies are small, particularly when stimuli are matched for intensity, implying that ICMS-based somatosensory feedback is rapid enough to be effective in neuroprosthetic applications.

RevDate: 2022-06-01

Begnoche JP, Schilling KG, Boyd BD, et al (2022)

EPI susceptibility correction introduces significant differences far from local areas of high distortion.

Magnetic resonance imaging pii:S0730-725X(22)00081-9 [Epub ahead of print].

PURPOSE: In echo-planar diffusion-weighted imaging, correcting for susceptibility-induced artifacts typically requires acquiring pairs of images, known as blip-up blip-down acquisitions, to create an undistorted volume as a target to correct distortions that are often focal where regions with differences in magnetic susceptibility interface, such as the frontal and temporal areas. However, blip-up blip-down acquisitions are not always available, and distortion effects may not be specifically localized to such areas, with subtle effects potentially extending throughout the brain. Here, we apply a deep learning technique to generate an undistorted volume to correct susceptibility-induced artifacts and demonstrate implications for image fidelity and diffusion-based inference outside of areas where high focal distortion is present.

METHODS: To demonstrate differences due to susceptibility artifact correction, uncorrected baseline images were compared to identical images where correction was performed using an undistorted target volume produced by the deep learning tool "PreQual". Widespread geometric distortion was assessed visually by referencing diffusion-weighted images to T1-weighted images. Tract-based spatial statistics (TBSS) were utilized to perform whole brain analysis of fractional anisotropy (FA) values to assess differences between subject groups (depressed vs. non-depressed) via permutation-based, voxel-wise testing. Multivariate regression models were then used to contrast TBSS results between corrected and non-corrected diffusion images.

RESULTS: Susceptibility artifact correction resulted in visible, widespread improvement in image fidelity when referenced to T1-weighted images. TBSS results were dependent on susceptibility artifact correction with correction resulting in widespread structural alterations of the mean FA skeleton, changes in skeletal FA, and additional positive tests of significance of regression coefficients in subsequent regression models.

CONCLUSION: Our results indicated that EPI distortion effects are not purely focal, and that reducing distortion can result in significant differences in the interpretation of diffusion data, even in areas remote from high distortion.

RevDate: 2022-05-31

Liu B, Wang Y, Gao X, et al (2022)

eldBETA: A Large Eldercare-oriented Benchmark Database of SSVEP-BCI for the Aging Population.

Scientific data, 9(1):252.

Global population aging poses an unprecedented challenge and calls for a rising effort in eldercare and healthcare. Steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) boasts its high transfer rate and shows great promise in real-world applications to support aging. Public database is critically important for designing the SSVEP-BCI systems. However, the SSVEP-BCI database tailored for the elder is scarce in existing studies. Therefore, in this study, we present a large eldercare-oriented BEnchmark database of SSVEP-BCI for The Aging population (eldBETA). The eldBETA database consisted of the 64-channel electroencephalogram (EEG) from 100 elder participants, each of whom performed seven blocks of 9-target SSVEP-BCI task. The quality and characteristics of the eldBETA database were validated by a series of analyses followed by a classification analysis of thirteen frequency recognition methods. We expect that the eldBETA database would provide a substrate for the design and optimization of the BCI systems intended for the elders. The eldBETA database is open-access for research and can be downloaded from the website https://doi.org/10.6084/m9.figshare.18032669 .

RevDate: 2022-05-31

Arpaia P, Esposito A, Natalizio A, et al (2022)

How to successfully classify EEG in motor imagery BCI: a metrological analysis of the state of the art.

Journal of neural engineering [Epub ahead of print].

Objective. Processing strategies are analysed with respect to the classification of electroencephalographic signals related to brain-computer interfaces based on motor imagery. A review of literature is carried out to understand the achievements in motor imagery classification, the most promising trends, and the challenges in replicating these results. Main focus is placed on performance by means of a rigorous metrological analysis carried out in compliance with the international vocabulary of metrology. Hence, classification accuracy and its uncertainty are considered, as well as repeatability and reproducibility.Approach. The paper works included in the review concern the classification of electroencephalographic signals in motor-imagery- based brain-computer interfaces. Article search was carried out in accordance with the PRISMA standard and 89 studies were included.Main results. Statistically-based analyses show that brain-inspired approaches are increasingly proposed, and that these are particularly successful in discriminating against multiple classes. Notably, many proposals involve convolutional neural networks. Instead, classical machine learning approaches are still effective for binary classifications. Many proposals combine common spatial pattern, least absolute shrinkage and selection operator, and support vector machines. Regarding reported classification accuracies, performance above the upper quartile is in the 85 % to 100 % range for the binary case and in the 83 % to 93 % range for multi-class one. Associated uncertainties are up to 6 % while repeatability for a predetermined dataset is up to 8 %. Reproducibility assessment was instead prevented by lack of standardization in experiments.Significance. By relying on the analysed studies, the reader is guided towards the development of a successful processing strategy as a crucial part of a brain-computer interface. Moreover, it is suggested that future studies should extend these approaches on data from more subjects and with custom experiments, even by investigating online operation. This would also enable the quantification of results reproducibility.

RevDate: 2022-05-31

He W, Tang H, Li J, et al (2022)

Feature-based Quality Assessment of Middle Cerebral Artery Occlusion Using 18F-Fluorodeoxyglucose Positron Emission Tomography.

Neuroscience bulletin [Epub ahead of print].

In animal experiments, ischemic stroke is usually induced through middle cerebral artery occlusion (MCAO), and quality assessment of this procedure is crucial. However, an accurate assessment method based on 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is still lacking. The difficulty lies in the inconsistent preprocessing pipeline, biased intensity normalization, or unclear spatiotemporal uptake of FDG. Here, we propose an image feature-based protocol to assess the quality of the procedure using a 3D scale-invariant feature transform and support vector machine. This feature-based protocol provides a convenient, accurate, and reliable tool to assess the quality of the MCAO procedure in FDG PET studies. Compared with existing approaches, the proposed protocol is fully quantitative, objective, automatic, and bypasses the intensity normalization step. An online interface was constructed to check images and obtain assessment results.

RevDate: 2022-05-31

Dinh TH, Singh AK, Trung NL, et al (2022)

EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-based Ranking.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).

RevDate: 2022-05-31

Kulkarni V, Joshi Y, Manthalkar R, et al (2022)

Band decomposition of asynchronous electroencephalogram signal for upper limb movement classification.

Physical and engineering sciences in medicine [Epub ahead of print].

Decoding asynchronous electroencephalogram (A-EEG) signals is a crucial challenge in the emerging field of EEG based brain-computer interface. In the case of A-EEG signals, the time markers of motor activity are absent. The paper proposes a method to decompose the A-EEG signals using gabor elementary function designed with Gabor frames. The scale-space analysis extracts Gabor dominant frequencies from A-EEG signals. Statistical and temporal moment dependent features are used to create the feature vector for each estimated gabor band. The statistical significance of the features is tested with the Kruskal-Wallis test. The deep neural network is implemented with bi-directional long short-term memory block to classify the upper limb movement. The EEG data of healthy volunteers have been collected using the Enobio-20 electrode system and ArmeoSpring rehabilitation device. The proposed methodology has achieved an average classification accuracy of 96.83%, precision 0.96, recall 0.96, and F1-score of 0.93 on the acquired data set. The designed framework for decoding upper limb movement outperforms the existing state-of-the-art methods. In the future, the proposed framework could increase classification performance by incorporating multiple types of biological inputs for investigating various brain functions.

RevDate: 2022-05-31

Zhang LN, DU XX, Zhang YT, et al (2022)

[A comparative study of microwire electrode array with built-in and external reference electrodes].

Zhongguo ying yong sheng li xue za zhi = Zhongguo yingyong shenglixue zazhi = Chinese journal of applied physiology, 38(1):85-90.

Objective: To compare the difference between the built-in and external reference electrode of microwire electrode array in the process of recording rat brain neuron firings, optimizing the production and embedding of the microwire electrode array, and providing a more affordable and excellent media tool for multi-channel electrophysiological real-time recording system. Methods: A 16 channel microwire electrode array was made by using nickel chromium alloy wires, circuit board, electrode pin and ground wires (silver wires). The reference electrode of the microwire electrode array was built-in (the reference electrode and electrode array were arranged in parallel) or external (the reference electrode and ground wire were welded at both ends of one side of the electrode), and the difference between the two electrodes was observed and compared in recording neuronal discharges in ACC brain area of rats. Experimental rats were divided into built-in group and external group, n=8-9. The test indicators included signal-to-noise ratio (n=8), discharge amplitude (n=380) and discharge frequency (n=54). Results: The microwire electrode array with both built-in and external reference electrodes successfully recorded the electrical signals of neurons in the ACC brain region of rats. Compared with the external group, the electrical signals of neurons in built-in group had the advantages of a higher signal-to-noise ratio (P<0.05), a smaller amplitude of background signals and less noise interference, and a larger discharge amplitude(P<0.05); there was no significant difference in spike discharge frequency recorded by these two types of electrodes (P>0.05). Conclusion: When recording the electrical activity of neurons in the ACC brain region of rats, the microwire electrode array with built-in reference electrode recorded electrical signals with higher signal-to-noise ratio and larger discharge amplitude, providing a more reliable tool for multi-channel electrophysiology technology.

RevDate: 2022-05-31

Al-Nafjan A (2022)

Feature selection of EEG signals in neuromarketing.

PeerJ. Computer science, 8:e944 pii:cs-944.

Brain-computer interface (BCI) technology uses electrophysiological (EEG) signals to detect user intent. Research on BCI has seen rapid advancement, with researchers proposing and implementing several signal processing and machine learning approaches for use in different contexts. BCI technology is also used in neuromarketing to study the brain's responses to marketing stimuli. This study sought to detect two preference states (like and dislike) in EEG neuromarketing data using the proposed EEG-based consumer preference recognition system. This study investigated the role of feature selection in BCI to improve the accuracy of preference detection for neuromarketing. Several feature selection methods were used for benchmark testing in multiple BCI studies. Four feature selection approaches, namely, principal component analysis (PCA), minimum redundancy maximum relevance (mRMR), recursive feature elimination (RFE), and ReliefF, were used with five different classifiers: deep neural network (DNN), support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forest (RF). The four approaches were compared to evaluate the importance of feature selection. Moreover, the performance of classification algorithms was evaluated before and after feature selection. It was found that feature selection for EEG signals improves the performance of all classifiers.

RevDate: 2022-05-31
CmpDate: 2022-05-31

Tang Z, Zhang L, Chen X, et al (2022)

Wearable Supernumerary Robotic Limb System Using a Hybrid Control Approach Based on Motor Imagery and Object Detection.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 30:1298-1309.

Motor disorder of upper limbs has seriously affected the daily life of the patients with hemiplegia after stroke. We developed a wearable supernumerary robotic limb (SRL) system using a hybrid control approach based on motor imagery (MI) and object detection for upper-limb motion assistance. SRL system included an SRL hardware subsystem and a hybrid control software subsystem. The system obtained the patient's motion intention through MI electroencephalogram (EEG) recognition method based on graph convolutional network (GCN) and gated recurrent unit network (GRU) to control the left and right movements of SRL, and the object detection technology was used together for a quick grasp of target objects to compensate for the disadvantages when using MI EEG alone like fewer control instructions and lower control efficiency. Offline training experiment was designed to obtain subjects' MI recognition models and evaluate the feasibility of the MI EEG recognition method; online control experiment was designed to verify the effectiveness of our wearable SRL system. The results showed that the proposed MI EEG recognition method (GCN+GRU) could effectively improve the MI classification accuracy (90.04% ± 2.36 %) compared with traditional methods; all subjects were able to complete the target object grasping tasks within 23 seconds by controlling the SRL, and the highest average grasping success rate achieved 90.67% in bag grasping task. The SRL system can effectively assist people with upper-limb motor disorder to perform upper-limb tasks in daily life by natural human-robot interaction, and improve their ability of self-help and enhance their confidence of life.

RevDate: 2022-05-28

Hopfgartner A, Burns D, Suppiah S, et al (2022)

Bullseye EVD: preclinical evaluation of an intra-procedural system to confirm external ventricular drainage catheter positioning.

International journal of computer assisted radiology and surgery [Epub ahead of print].

PURPOSE: External ventricular drainage (EVD) is a life-saving procedure indicated for elevated intracranial pressure. A catheter is inserted into the ventricles to drain cerebrospinal fluid and release the pressure on the brain. However, the standard freehand EVD technique results in catheter malpositioning in up to 60.1% of procedures. This proof-of-concept study aimed to evaluate the registration accuracy of a novel image-based verification system "Bullseye EVD" in a preclinical cadaveric model of catheter placement.

METHODS: Experimentation was performed on both sides of 3 cadaveric heads (n = 6). After a pre-interventional CT scan, a guidewire simulating the EVD catheter was inserted as in a clinical EVD procedure. 3D structured light images (Einscan, Shining 3D, China) were acquired of an optical tracker placed over the guidewire on the surface of the scalp, along with three distinct cranial regions (scalp, face, and ear). A computer vision algorithm was employed to determine the guidewire position based on the pre-interventional CT scan and the intra-procedural optical imaging. A post-interventional CT scan was used to validate the performance of the Bullseye optical imaging system in terms of trajectory and offset errors.

RESULTS: Optical images which combined facial features and exposed scalp within the surgical field resulted in the lowest trajectory and offset errors of 1.28° ± 0.38° and 0.33 ± 0.19 mm, respectively. Mean duration of the optical imaging procedure was 128 ± 35 s.

CONCLUSIONS: The Bullseye EVD system presents an accurate patient-specific method to verify freehand EVD positioning. Use of facial features was critical to registration accuracy. Workflow automation and development of a user interface must be considered for future clinical evaluation.

RevDate: 2022-05-28

Kurmi A, Biswas S, Sen S, et al (2022)

An Ensemble of CNN Models for Parkinson's Disease Detection Using DaTscan Images.

Diagnostics (Basel, Switzerland), 12(5): pii:diagnostics12051173.

Parkinson's Disease (PD) is a progressive central nervous system disorder that is caused due to the neural degeneration mainly in the substantia nigra in the brain. It is responsible for the decline of various motor functions due to the loss of dopamine-producing neurons. Tremors in hands is usually the initial symptom, followed by rigidity, bradykinesia, postural instability, and impaired balance. Proper diagnosis and preventive treatment can help patients improve their quality of life. We have proposed an ensemble of Deep Learning (DL) models to predict Parkinson's using DaTscan images. Initially, we have used four DL models, namely, VGG16, ResNet50, Inception-V3, and Xception, to classify Parkinson's disease. In the next stage, we have applied a Fuzzy Fusion logic-based ensemble approach to enhance the overall result of the classification model. The proposed model is assessed on a publicly available database provided by the Parkinson's Progression Markers Initiative (PPMI). The achieved recognition accuracy, Precision, Sensitivity, Specificity, F1-score from the proposed model are 98.45%, 98.84%, 98.84%, 97.67%, and 98.84%, respectively which are higher than the individual model. We have also developed a Graphical User Interface (GUI)-based software tool for public use that instantly detects all classes using Magnetic Resonance Imaging (MRI) with reasonable accuracy. The proposed method offers better performance compared to other state-of-the-art methods in detecting PD. The developed GUI-based software tool can play a significant role in detecting the disease in real-time.

RevDate: 2022-05-28

Jiang Q, Zhang Y, K Zheng (2022)

Motor Imagery Classification via Kernel-Based Domain Adaptation on an SPD Manifold.

Brain sciences, 12(5): pii:brainsci12050659.

BACKGROUND: Recording the calibration data of a brain-computer interface is a laborious process and is an unpleasant experience for the subjects. Domain adaptation is an effective technology to remedy the shortage of target data by leveraging rich labeled data from the sources. However, most prior methods have needed to extract the features of the EEG signal first, which triggers another challenge in BCI classification, due to small sample sets or a lack of labels for the target.

METHODS: In this paper, we propose a novel domain adaptation framework, referred to as kernel-based Riemannian manifold domain adaptation (KMDA). KMDA circumvents the tedious feature extraction process by analyzing the covariance matrices of electroencephalogram (EEG) signals. Covariance matrices define a symmetric positive definite space (SPD) that can be described by Riemannian metrics. In KMDA, the covariance matrices are aligned in the Riemannian manifold, and then are mapped to a high dimensional space by a log-Euclidean metric Gaussian kernel, where subspace learning is performed by minimizing the conditional distribution distance between the sources and the target while preserving the target discriminative information. We also present an approach to convert the EEG trials into 2D frames (E-frames) to further lower the dimension of covariance descriptors.

RESULTS: Experiments on three EEG datasets demonstrated that KMDA outperforms several state-of-the-art domain adaptation methods in classification accuracy, with an average Kappa of 0.56 for BCI competition IV dataset IIa, 0.75 for BCI competition IV dataset IIIa, and an average accuracy of 81.56% for BCI competition III dataset IVa. Additionally, the overall accuracy was further improved by 5.28% with the E-frames. KMDA showed potential in addressing subject dependence and shortening the calibration time of motor imagery-based brain-computer interfaces.

RevDate: 2022-05-27

Williams SC, Horsfall HL, Funnell JP, et al (2022)

Neurosurgical team acceptability of brain-computer interfaces: a two-stage international cross-sectional survey.

World neurosurgery pii:S1878-8750(22)00692-1 [Epub ahead of print].

OBJECTIVE: Invasive brain-computer interfaces (BCIs) require neurosurgical implantation, which confers a range of risks. Despite this, no studies have assessed the acceptability of invasive BCIs amongst the neurosurgical team. This study aims to establish baseline knowledge of BCIs within the neurosurgical team and identify attitudes towards different applications of invasive BCI.

METHOD: A two-stage cross-sectional international survey of the neurosurgical team (neurosurgeons, anaesthetists, and operating room nurses) was conducted. Results from the first, qualitative, survey were used to guide the second stage quantitative survey, which assessed acceptability of invasive BCI applications. 5-part Likert Scales were used to collect quantitative data. Surveys were distributed internationally via social media and collaborators.

RESULTS: 108 qualitative responses were collected. Themes included the promise of BCIs positively impacting disease targets, concerns regarding stability, and an overall positive emotional reaction to BCI technology. The quantitative survey generated 538 responses from 32 countries. Baseline knowledge of BCI technology was poor, with 9% claiming to have a 'good' or 'expert' knowledge of BCIs. Acceptability of invasive BCI for rehabilitative purposes was >80%. Invasive BCI for augmentation in healthy populations divided opinion.

CONCLUSION: The neurosurgical team's view of the acceptability of BCI was divided across a range of indications. Some applications (for example stroke rehabilitation) were viewed as more appropriate than other applications (such as augmentation for military use). This range in views highlights the need for stakeholder consultation on acceptable use cases along with regulation and guidance to govern initial BCI implantations if patients are to realise the potential benefits.

RevDate: 2022-05-27

Merk T, Peterson V, Lipski WJ, et al (2022)

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease.

eLife, 11: pii:75126.

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson's disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.

RevDate: 2022-05-27

Tonin L, Beraldo G, Tortora S, et al (2022)

ROS-Neuro: An Open-Source Platform for Neurorobotics.

Frontiers in neurorobotics, 16:886050.

The growing interest in neurorobotics has led to a proliferation of heterogeneous neurophysiological-based applications controlling a variety of robotic devices. Although recent years have seen great advances in this technology, the integration between human neural interfaces and robotics is still limited, making evident the necessity of creating a standardized research framework bridging the gap between neuroscience and robotics. This perspective paper presents Robot Operating System (ROS)-Neuro, an open-source framework for neurorobotic applications based on ROS. ROS-Neuro aims to facilitate the software distribution, the repeatability of the experimental results, and support the birth of a new community focused on neuro-driven robotics. In addition, the exploitation of Robot Operating System (ROS) infrastructure guarantees stability, reliability, and robustness, which represent fundamental aspects to enhance the translational impact of this technology. We suggest that ROS-Neuro might be the future development platform for the flourishing of a new generation of neurorobots to promote the rehabilitation, the inclusion, and the independence of people with disabilities in their everyday life.


RJR Experience and Expertise


Robbins holds BS, MS, and PhD degrees in the life sciences. He served as a tenured faculty member in the Zoology and Biological Science departments at Michigan State University. He is currently exploring the intersection between genomics, microbial ecology, and biodiversity — an area that promises to transform our understanding of the biosphere.


Robbins has extensive experience in college-level education: At MSU he taught introductory biology, genetics, and population genetics. At JHU, he was an instructor for a special course on biological database design. At FHCRC, he team-taught a graduate-level course on the history of genetics. At Bellevue College he taught medical informatics.


Robbins has been involved in science administration at both the federal and the institutional levels. At NSF he was a program officer for database activities in the life sciences, at DOE he was a program officer for information infrastructure in the human genome project. At the Fred Hutchinson Cancer Research Center, he served as a vice president for fifteen years.


Robbins has been involved with information technology since writing his first Fortran program as a college student. At NSF he was the first program officer for database activities in the life sciences. At JHU he held an appointment in the CS department and served as director of the informatics core for the Genome Data Base. At the FHCRC he was VP for Information Technology.


While still at Michigan State, Robbins started his first publishing venture, founding a small company that addressed the short-run publishing needs of instructors in very large undergraduate classes. For more than 20 years, Robbins has been operating The Electronic Scholarly Publishing Project, a web site dedicated to the digital publishing of critical works in science, especially classical genetics.


Robbins is well-known for his speaking abilities and is often called upon to provide keynote or plenary addresses at international meetings. For example, in July, 2012, he gave a well-received keynote address at the Global Biodiversity Informatics Congress, sponsored by GBIF and held in Copenhagen. The slides from that talk can be seen HERE.


Robbins is a skilled meeting facilitator. He prefers a participatory approach, with part of the meeting involving dynamic breakout groups, created by the participants in real time: (1) individuals propose breakout groups; (2) everyone signs up for one (or more) groups; (3) the groups with the most interested parties then meet, with reports from each group presented and discussed in a subsequent plenary session.


Robbins has been engaged with photography and design since the 1960s, when he worked for a professional photography laboratory. He now prefers digital photography and tools for their precision and reproducibility. He designed his first web site more than 20 years ago and he personally designed and implemented this web site. He engages in graphic design as a hobby.

Support this website:
Order from Amazon
We will earn a commission.

Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

963 Red Tail Lane
Bellingham, WA 98226


E-mail: RJR8222@gmail.com

Collection of publications by R J Robbins

Reprints and preprints of publications, slide presentations, instructional materials, and data compilations written or prepared by Robert Robbins. Most papers deal with computational biology, genome informatics, using information technology to support biomedical research, and related matters.

Research Gate page for R J Robbins

ResearchGate is a social networking site for scientists and researchers to share papers, ask and answer questions, and find collaborators. According to a study by Nature and an article in Times Higher Education , it is the largest academic social network in terms of active users.

Curriculum Vitae for R J Robbins

short personal version

Curriculum Vitae for R J Robbins

long standard version

RJR Picks from Around the Web (updated 11 MAY 2018 )