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28 Sep 2021 at 01:35
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Bibliography on: Brain-Computer Interface


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RJR: Recommended Bibliography 28 Sep 2021 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: 2021-09-27

Zhang K, Xu G, Du C, et al (2021)

Enhancement of capability for motor imagery using vestibular imbalance stimulation during brain computer interface.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Motor imagery (MI), based on the theory of mirror neuron and neuroplasticity, provide a promising way to promote motor cortical activation in neurorehabilitation. The strategy of MI based on brain-computer interface has been widely used in rehabilitation training and daily assistance for patients with hemiplegia. However, it was difficult to maintain the consistency and timeliness from receiving external stimulus to the neuronal activation for most subjects due to the highly variability of EEG representation across trials/subjects. Moreover, in practical application, MI-BCI cannot provide reliable control and hardly to highly activate the motor cortex due to the weakness of EEG feature and lack of joint modulation by multiple brain regions.

APPROACH: In this study, a novel hybrid brain-computer interface based on motor imagery and vestibular stimulation (VSMI) is proposed to enhance the capability of feature-response for MI. Twelve subjects participated a group of controlled experiments contains VSMI and MI. Three indicators contain activation degree, timeliness and classification accuracy were adopted to evaluate the performance of these task.

MAIN RESULTS: Results show that vestibular stimulation could significantly strengthen the suppression of α and β bands of contralateral brain regions during MI, i.e., enhance the activation degree of motor cortex(p<0.01). Compared with MI, the timeliness of feature-response of EEG had achieved obvious improvements in VSMI experiments. Meanwhile, the averaged classification accuracy of VSMI and MI could obtain 80.56% and 69.38% respectively.

SIGNIFICANCE: From experimental results, this study demonstrated that specific vestibular activity contributes to the oscillations of motor cortex and have a positive effect on spontaneous imagery, which provide a novel MI paradigm and has realized the preliminary exploration of sensorimotor integration during motor imagery.

RevDate: 2021-09-27

Chen H, Wang L, Lu Y, et al (2020)

Bioinspired microcone-array-based living biointerfaces: enhancing the anti-inflammatory effect and neuronal network formation.

Microsystems & nanoengineering, 6:58 pii:172.

Implantable neural interfaces and systems have attracted much attention due to their broad applications in treating diverse neuropsychiatric disorders. However, obtaining a long-term reliable implant-neural interface is extremely important but remains an urgent challenge due to the resulting acute inflammatory responses. Here, bioinspired microcone-array-based (MA) interfaces have been successfully designed, and their cytocompatibility with neurons and the inflammatory response have been explored. Compared with smooth control samples, MA structures cultured with neuronal cells result in much denser extending neurites, which behave similar to creepers, wrapping tightly around the microcones to form complex and interconnected neuronal networks. After further implantation in mouse brains for 6 weeks, the MA probes (MAPs) significantly reduced glial encapsulation and neuron loss around the implants, suggesting better neuron viability at the implant-neural interfaces than that of smooth probes. This bioinspired strategy for both enhanced glial resistance and neuron network formation via a specific structural design could be a platform technology that not only opens up avenues for next-generation artificial neural networks and brain-machine interfaces but also provides universal approaches to biomedical therapeutics.

RevDate: 2021-09-27

Hong S, Giese AK, Schirmer MD, et al (2021)

Excessive White Matter Hyperintensity Increases Susceptibility to Poor Functional Outcomes After Acute Ischemic Stroke.

Frontiers in neurology, 12:700616.

Objective: To personalize the prognostication of post-stroke outcome using MRI-detected cerebrovascular pathology, we sought to investigate the association between the excessive white matter hyperintensity (WMH) burden unaccounted for by the traditional stroke risk profile of individual patients and their long-term functional outcomes after a stroke. Methods: We included 890 patients who survived after an acute ischemic stroke from the MRI-Genetics Interface Exploration (MRI-GENIE) study, for whom data on vascular risk factors (VRFs), including age, sex, atrial fibrillation, diabetes mellitus, hypertension, coronary artery disease, smoking, prior stroke history, as well as acute stroke severity, 3- to-6-month modified Rankin Scale score (mRS), WMH, and brain volumes, were available. We defined the unaccounted WMH (uWMH) burden via modeling of expected WMH burden based on the VRF profile of each individual patient. The association of uWMH and mRS score was analyzed by linear regression analysis. The odds ratios of patients who achieved full functional independence (mRS < 2) in between trichotomized uWMH burden groups were calculated by pair-wise comparisons. Results: The expected WMH volume was estimated with respect to known VRFs. The uWMH burden was associated with a long-term functional outcome (β = 0.104, p < 0.01). Excessive uWMH burden significantly reduced the odds of achieving full functional independence after a stroke compared to the low and average uWMH burden [OR = 0.4, 95% CI: (0.25, 0.63), p < 0.01 and OR = 0.61, 95% CI: (0.42, 0.87), p < 0.01, respectively]. Conclusion: The excessive amount of uWMH burden unaccounted for by the traditional VRF profile was associated with worse post-stroke functional outcomes. Further studies are needed to evaluate a lifetime brain injury reflected in WMH unrelated to the VRF profile of a patient as an important factor for stroke recovery and a plausible indicator of brain health.

RevDate: 2021-09-27

Park S, Kim DW, Han CH, et al (2021)

Estimation of Emotional Arousal Changes of a Group of Individuals During Movie Screening Using Steady-State Visual-Evoked Potential.

Frontiers in neuroinformatics, 15:731236.

Neurocinematics is an emerging discipline in neuroscience, which aims to provide new filmmaking techniques by analyzing the brain activities of a group of audiences. Several neurocinematics studies attempted to track temporal changes in mental states during movie screening; however, it is still needed to develop efficient and robust electroencephalography (EEG) features for tracking brain states precisely over a long period. This study proposes a novel method for estimating emotional arousal changes in a group of individuals during movie screening by employing steady-state visual evoked potential (SSVEP), which is a widely used EEG response elicited by the presentation of periodic visual stimuli. Previous studies have reported that the emotional arousal of each individual modulates the strength of SSVEP responses. Based on this phenomenon, movie clips were superimposed on a background, eliciting an SSVEP response with a specific frequency. Two emotionally arousing movie clips were presented to six healthy male participants, while EEG signals were recorded from the occipital channels. We then investigated whether the movie scenes that elicited higher SSVEP responses coincided well with those rated as the most impressive scenes by 37 viewers in a separate experimental session. Our results showed that the SSVEP response averaged across six participants could accurately predict the overall impressiveness of each movie, evaluated with a much larger group of individuals.

RevDate: 2021-09-27

Li S, Lyu X, Zhao L, et al (2021)

Identification of Emotion Using Electroencephalogram by Tunable Q-Factor Wavelet Transform and Binary Gray Wolf Optimization.

Frontiers in computational neuroscience, 15:732763.

Emotional brain-computer interface based on electroencephalogram (EEG) is a hot issue in the field of human-computer interaction, and is also an important part of the field of emotional computing. Among them, the recognition of EEG induced by emotion is a key problem. Firstly, the preprocessed EEG is decomposed by tunable-Q wavelet transform. Secondly, the sample entropy, second-order differential mean, normalized second-order differential mean, and Hjorth parameter (mobility and complexity) of each sub-band are extracted. Then, the binary gray wolf optimization algorithm is used to optimize the feature matrix. Finally, support vector machine is used to train the classifier. The five types of emotion signal samples of 32 subjects in the database for emotion analysis using physiological signal dataset is identified by the proposed algorithm. After 6-fold cross-validation, the maximum recognition accuracy is 90.48%, the sensitivity is 70.25%, the specificity is 82.01%, and the Kappa coefficient is 0.603. The results show that the proposed method has good performance indicators in the recognition of multiple types of EEG emotion signals, and has a better performance improvement compared with the traditional methods.

RevDate: 2021-09-27

Zheng M, B Yang (2021)

A deep neural network with subdomain adaptation for motor imagery brain-computer interface.

Medical engineering & physics, 96:29-40.

BACKGROUND: The nonstationarity problem of EEG is very serious, especially for spontaneous signals, which leads to the poor effect of machine learning related to spontaneous signals, especially in related tasks across time, which correspondingly limits the practical use of brain-computer interface (BCI).

OBJECTIVE: In this paper, we proposed a new transfer learning algorithm, which can utilize the labeled motor imagery (MI) EEG data at the previous time to achieve better classification accuracies for a small number of labeled EEG signals at the current time.

METHODS: We introduced an adaptive layer into the full connection layer of a deep convolution neural network. The objective function of the adaptive layer was designed to minimize the Local Maximum Mean Discrepancy (LMMD) and the prediction error while minimizing the distance within each class (DWC) and maximizing the distance between classes within each domain (DBCWD). We verified the effectiveness of the proposed algorithm on two public datasets.

RESULTS: The classification accuracy of the proposed algorithm was higher than other comparison algorithms, and the paired t-test results also showed that the performance of the proposed algorithm was significantly different from that of other algorithms. The results of the confusion matrix and feature visualization showed the effectiveness of the proposed algorithm.

CONCLUSION: Experimental results showed that the proposed algorithm can achieve higher classification accuracy than other algorithms when there was only a small amount of labeled MI EEG data at the current time. It can be promising to be applied to the field of BCI.

RevDate: 2021-09-26

Aellen FM, Göktepe-Kavis P, Apostolopoulos S, et al (2021)

Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features.

Journal of neuroscience methods pii:S0165-0270(21)00302-2 [Epub ahead of print].

BACKGROUND: Deep learning has revolutionized the field of computer vision, where convolutional neural networks (CNNs) extract complex patterns of information from large datasets. The use of deep networks in neuroscience is mainly focused to neuroimaging or brain computer interface -BCI- applications. In electroencephalography (EEG) research, multivariate pattern analysis (MVPA) mainly relies on linear algorithms, which require a homogeneous dataset and assume that discriminant features appear at consistent latencies and electrodes across trials. However, neural responses may shift in time or space during an experiment, resulting in under-estimation of discriminant features. Here, we aimed at using CNNs to classify EEG responses to external stimuli, by taking advantage of time- and space- unlocked neural activity, and at examining how discriminant features change over the course of an experiment, on a trial by trial basis.

NEW METHOD: We present a novel pipeline, consisting of data augmentation, CNN training, and feature visualization techniques, fine-tuned for MVPA on EEG data.

RESULTS: Our pipeline provides high classification performance and generalizes to new datasets. Additionally, we show that the features identified by the CNN for classification are electrophysiologically interpretable and can be reconstructed at the single-trial level to study trial-by-trial evolution of class-specific discriminant activity.

The developed pipeline was compared to commonly used MVPA algorithms like logistic regression and support vector machines, as well as to shallow and deep convolutional neural networks. Our approach yielded significantly higher classification performance than existing MVPA techniques (p = 0.006) and comparable results to other CNNs for EEG data.

CONCLUSION: In summary, we present a novel deep learning pipeline for MVPA of EEG data, that can extract trial-by-trial discriminative activity in a data-driven way.

RevDate: 2021-09-25

Lin CT, Jiang WL, Chen SF, et al (2021)

Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation.

Biosensors, 11(9): pii:bios11090343.

In the assistive research area, human-computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life applications.

RevDate: 2021-09-25

Wen S, Yin A, Tseng PH, et al (2021)

Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface.

Scientific reports, 11(1):19020.

Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description-wavelet average coefficients (WAC)-to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders (Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

RevDate: 2021-09-25

Ingel A, R Vicente (2021)

Information Bottleneck as Optimisation Method for SSVEP-Based BCI.

Frontiers in human neuroscience, 15:675091.

In this study, the information bottleneck method is proposed as an optimisation method for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). The information bottleneck is an information-theoretic optimisation method for solving problems with a trade-off between preserving meaningful information and compression. Its main practical application in machine learning is in representation learning or feature extraction. In this study, we use the information bottleneck to find optimal classification rule for a BCI. This is a novel application for the information bottleneck. This approach is particularly suitable for BCIs since the information bottleneck optimises the amount of information transferred by the BCI. Steady-state visual evoked potential-based BCIs often classify targets using very simple rules like choosing the class corresponding to the largest feature value. We call this classifier the arg max classifier. It is unlikely that this approach is optimal, and in this study, we propose a classification method specifically designed to optimise the performance measure of BCIs. This approach gives an advantage over standard machine learning methods, which aim to optimise different measures. The performance of the proposed algorithm is tested on two publicly available datasets in offline experiments. We use the standard power spectral density analysis (PSDA) and canonical correlation analysis (CCA) feature extraction methods on one dataset and show that the current approach outperforms most of the related studies on this dataset. On the second dataset, we use the task-related component analysis (TRCA) method and demonstrate that the proposed method outperforms the standard argmax classification rule in terms of information transfer rate when using a small number of classes. To our knowledge, this is the first time the information bottleneck is used in the context of SSVEP-based BCIs. The approach is unique in the sense that optimisation is done over the space of classification functions. It potentially improves the performance of BCIs and makes it easier to calibrate the system for different subjects.

RevDate: 2021-09-23

Maziero D, Stenger VA, DW Carmichael (2021)

Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion.

Brain topography [Epub ahead of print].

The data quality of simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) can be strongly affected by motion. Recent work has shown that the quality of fMRI data can be improved by using a Moiré-Phase-Tracker (MPT)-camera system for prospective motion correction. The use of the head position acquired by the MPT-camera-system has also been shown to correct motion-induced voltages, ballistocardiogram (BCG) and gradient artefact residuals separately. In this work we show the concept of an integrated framework based on the general linear model to provide a unified motion informed model of in-MRI artefacts. This model (retrospective EEG motion educated gradient artefact suppression, REEG-MEGAS) is capable of correcting voltage-induced, BCG and gradient artefact residuals of EEG data acquired simultaneously with prospective motion corrected fMRI. In our results, we have verified that applying REEG-MEGAS correction to EEG data acquired during subject motion improves the data quality in terms of motion induced voltages and also GA residuals in comparison to standard Artefact Averaging Subtraction and Retrospective EEG Motion Artefact Suppression. Besides that, we provide preliminary evidence that although adding more regressors to a model may slightly affect the power of physiological signals such as the alpha-rhythm, its application may increase the overall quality of a dataset, particularly when strongly affected by motion. This was verified by analysing the EEG traces, power spectra density and the topographic distribution from two healthy subjects. We also have verified that the correction by REEG-MEGAS improves higher frequency artefact correction by decreasing the power of Gradient Artefact harmonics. Our method showed promising results for decreasing the power of artefacts for frequencies up to 250 Hz. Additionally, REEG-MEGAS is a hybrid framework that can be implemented for real time prospective motion correction of EEG and fMRI data. Among other EEG-fMRI applications, the approach described here may benefit applications such as EEG-fMRI neurofeedback and brain computer interface, which strongly rely on the prospective acquisition and application of motion artefact removal.

RevDate: 2021-09-25

Zhang H, Guilleminot J, LJ Gomez (2021)

Stochastic modeling of geometrical uncertainties on complex domains, with application to additive manufacturing and brain interface geometries.

Computer methods in applied mechanics and engineering, 385:.

We present a stochastic modeling framework to represent and simulate spatially-dependent geometrical uncertainties on complex geometries. While the consideration of random geometrical perturbations has long been a subject of interest in computational engineering, most studies proposed so far have addressed the case of regular geometries such as cylinders and plates. Here, standard random field representations, such as Kahrunen-Loève expansions, can readily be used owing, in particular, to the relative simplicity to construct covariance operators on regular shapes. On the contrary, applying such techniques on arbitrary, non-convex domains remains difficult in general. In this work, we formulate a new representation for spatially-correlated geometrical uncertainties that allows complex domains to be efficiently handled. Building on previous contributions by the authors, the approach relies on the combination of a stochastic partial differential equation approach, introduced to capture salient features of the underlying geometry such as local curvature and singularities on the fly, and an information-theoretic model, aimed to enforce non-Gaussianity. More specifically, we propose a methodology where the interface of interest is immersed into a fictitious domain, and define algorithmic procedures to directly sample random perturbations on the manifold. A simple strategy based on statistical conditioning is also presented to update realizations and prevent self-intersections in the perturbed finite element mesh. We finally provide challenging examples to demonstrate the robustness of the framework, including the case of a gyroid structure produced by additive manufacturing and brain interfaces in patient-specific geometries. In both applications, we discuss suitable parameterization for the filtering operator and quantify the impact of the uncertainties through forward propagation.

RevDate: 2021-09-23

Duan J, Xu P, Cheng X, et al (2021)

Structures of full-length glycoprotein hormone receptor signalling complexes.

Nature [Epub ahead of print].

Luteinizing hormone and chorionic gonadotropin are glycoprotein hormones that are related to follicle-stimulating hormone and thyroid-stimulating hormone1,2. Luteinizing hormone and chorionic gonadotropin are essential to human reproduction and are important therapeutic drugs3-6. They activate the same G-protein-coupled receptor, luteinizing hormone-choriogonadotropin receptor (LHCGR), by binding to the large extracellular domain3. Here we report four cryo-electron microscopy structures of LHCGR: two structures of the wild-type receptor in the inactive and active states; and two structures of the constitutively active mutated receptor. The active structures are bound to chorionic gonadotropin and the stimulatory G protein (Gs), and one of the structures also contains Org43553, an allosteric agonist7. The structures reveal a distinct 'push-and-pull' mechanism of receptor activation, in which the extracellular domain is pushed by the bound hormone and pulled by the extended hinge loop next to the transmembrane domain. A highly conserved 10-residue fragment (P10) from the hinge C-terminal loop at the interface between the extracellular domain and the transmembrane domain functions as a tethered agonist to induce conformational changes in the transmembrane domain and G-protein coupling. Org43553 binds to a pocket of the transmembrane domain and interacts directly with P10, which further stabilizes the active conformation. Together, these structures provide a common model for understanding the signalling of glycoprotein hormone receptors and a basis for drug discovery for endocrine diseases.

RevDate: 2021-09-23

Zweerings J, Sarasjärvi K, Mathiak KA, et al (2021)

Data-Driven Approach to the Analysis of Real-Time FMRI Neurofeedback Data: Disorder-Specific Brain Synchrony in PTSD.

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

Brain-computer interfaces (BCIs) can be used in real-time fMRI neurofeedback (rtfMRI NF) investigations to provide feedback on brain activity to enable voluntary regulation of the blood-oxygen-level dependent (BOLD) signal from localized brain regions. However, the temporal pattern of successful self-regulation is dynamic and complex. In particular, the general linear model (GLM) assumes fixed temporal model functions and misses other dynamics. We propose a novel data-driven analyses approach for rtfMRI NF using intersubject covariance (ISC) analysis. The potential of ISC was examined in a reanalysis of data from 21 healthy individuals and nine patients with post-traumatic stress-disorder (PTSD) performing up-regulation of the anterior cingulate cortex (ACC). ISC in the PTSD group differed from healthy controls in a network including the right inferior frontal gyrus (IFG). In both cohorts, ISC decreased throughout the experiment indicating the development of individual regulation strategies. ISC analyses are a promising approach to reveal novel information on the mechanisms involved in voluntary self-regulation of brain signals and thus extend the results from GLM-based methods. ISC enables a novel set of research questions that can guide future neurofeedback and neuroimaging investigations.

RevDate: 2021-09-22

Lioi G, Veliz A, Coloigner J, et al (2021)

The impact of Neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study.

Journal of neural engineering [Epub ahead of print].

Objective In this study, we assessed the impact of EEG-fMRI Neurofeedback (NF) training on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after a NF training protocol targeting localised motor areas. Approach We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the Dynamic Causal Modeling and Parametric Empirical Bayes frameworks for the estimation of directed connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas. Main results Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task. Significance To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.

RevDate: 2021-09-22

Singh SA, Meitei TG, Devi ND, et al (2021)

A deep neural network approach for P300 detection-based BCI using single-channel EEG scalogram images.

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

Brain-computer interfaces (BCIs) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design using multi-channel EEG signals. This paper proposed a novel framework for the automatic P300 detection-based BCI model using a single EEG electrode. In the present study, we introduced a denoising approach using the bandpass filter technique followed by the transformation of scalogram images using continuous wavelet transform. The derived images were trained and validated using a deep neural network based on the transfer learning approach. This paper presents a BCI model based on the deep network that delivers higher performance in terms of classification accuracy and bitrate for disabled subjects using a single-channel EEG signal. The proposed P300 based BCI model has the highest average information transfer rates of 13.23 to 26.48 bits/min for disabled subjects. The classification performance has shown that the deep network based on the transfer learning approach can offer comparable performance with other state-of-the-art-method.

RevDate: 2021-09-25

Chandrasekaran S, Fifer M, Bickel S, et al (2021)

Historical perspectives, challenges, and future directions of implantable brain-computer interfaces for sensorimotor applications.

Bioelectronic medicine, 7(1):14.

Almost 100 years ago experiments involving electrically stimulating and recording from the brain and the body launched new discoveries and debates on how electricity, movement, and thoughts are related. Decades later the development of brain-computer interface technology began, which now targets a wide range of applications. Potential uses include augmentative communication for locked-in patients and restoring sensorimotor function in those who are battling disease or have suffered traumatic injury. Technical and surgical challenges still surround the development of brain-computer technology, however, before it can be widely deployed. In this review we explore these challenges, historical perspectives, and the remarkable achievements of clinical study participants who have bravely forged new paths for future beneficiaries.

RevDate: 2021-09-20

Ming G, Pei W, Chen H, et al (2021)

Optimizing spatial properties of a new checkerboard-like visual stimulus for user-friendly SSVEP-based BCIs.

Journal of neural engineering [Epub ahead of print].

Objective.Low-frequency steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems with high performance are prone to cause visual discomfort and fatigue. High-frequency SSVEP-based BCI systems can alleviate the discomfort, but always obtain lower performance. This study optimized the spatial properties of a proposed checkerboard-like visual stimulus toward high-performance and user-friendly SSVEP-based BCI systems.Approach.On the one hand, two checkerboard-like stimuli with distinct spatial contrasts (the black- and white-background) were designed to balance the tradeoff between BCI performance and user experience and compared with the traditional flickering stimulus. On the other hand, the impacts of the spatial frequency of the new checkerboard-like stimulus on the flicker perception and the intensity of the elicited SSVEP were clarified. The SSVEP-based BCI systems were implemented based on the checkerboard-like stimuli under low-frequency and high-frequency conditions. The user experience for each stimulation pattern was estimated by questionnaires for subjective evaluation.Main results.The comparison results indicate that the black-background checkerboard-like stimulus with an optimized spatial frequency achieved comparable performance and enhanced visual comfort compared with the flickering stimulus. Furthermore, the online nine-target BCI system using the black-background checkerboard-like stimuli achieved averaged information transfer rates (ITRs) of 124.0±2.3 bits/min and 109.0 ± 20.4 bits/min with low-frequency and high-frequency stimulation respectively.Significance.The new checkerboard-like stimuli with optimized properties show superiority of system performance and user experience in implementing SSVEP-based BCI, which will promote its practical applications in communication and control.

RevDate: 2021-09-22

Liu B, Chen X, Shi N, et al (2021)

Improving the Performance of Individually Calibrated SSVEP-BCI by Task-Discriminant Component Analysis.

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

A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually-calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.

RevDate: 2021-09-20

Mladenovic J, Frey J, Pramij S, et al (2021)

Towards identifying optimal biased feedback for various user states and traits in motor imagery BCI.

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

OBJECTIVE: Neural self-regulation is necessary for achieving control over brain-computer interfaces (BCIs). This can be an arduous learning process esspecially for motor imagery BCI. Various training methods were proposed to assist users in accomplishing BCI control and increase performance. Notably the use of biased feedback, i.e. non-realistic representation of performance. Benefits of biased feedback on performance and learning vary between users (e.g. depending on their initial level of BCI control) and remain speculative. To disentangle the speculations, we investigate what personality type, initial state and calibration performance (CP) could benefit from biased feedback.

METHODS: We conduct an experiment (n=30 for 2 sessions). The feedback provided to each group (n=10) is either positively, negatively or not biased.

RESULTS: Statistical analyses suggest that interactions between bias and: 1) workload, 2) anxiety, and 3) self-control significantly affect online performance. For instance, low initial workload paired with negative bias is associated to higher peak performances (86%) than without any bias (69%). High anxiety relates negatively to performance no matter the bias (60%), while low anxiety matches best with negative bias (76%). For low CP, learning rate (LR) increases with negative bias only short term (LR=2%) as during the second session it severely drops (LR=-1%).

CONCLUSION: We unveil many interactions between said human factors and bias. Additionally, we use prediction models to confirm and reveal even more interactions.

SIGNIFICANCE: This paper is a first step towards identifying optimal biased feedback for a personality type, state, and CP in order to maximize BCI performance and learning.

RevDate: 2021-09-21

Yan Y, Zhou H, Huang L, et al (2021)

A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification.

Frontiers in neuroscience, 15:657540.

Cerebral stroke is a common disease across the world, and it is a promising method to recognize the intention of stroke patients with the help of brain-computer interface (BCI). In the field of motor imagery (MI) classification, appropriate filtering is vital for feature extracting of electroencephalogram (EEG) signals and consequently influences the accuracy of MI classification. In this case, a novel two-stage refine filtering method was proposed, inspired by Gradient-weighted Class Activation Mapping (Grad-CAM), which uses the gradients of any target concept flowing into the final convolutional layer to highlight the important part of training data for predicting the concept. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated according to the Grad-CAM of the MI classification results. In the second stage, EEG was filtered and classified for a higher classification accuracy. To evaluate the filtering effect, this method was applied to the multi-branch neural network proposed in our previous work. Experiment results revealed that the proposed method reached state-of-the-art classification kappa value levels and acquired at least 3% higher kappa values than other methods This study also proposed some promising application scenarios with this filtering method.

RevDate: 2021-09-22

Liu X, Bibineyshvili Y, Robles DA, et al (2021)

Fabrication of a Multilayer Implantable Cortical Microelectrode Probe to Improve Recording Potential.

Journal of microelectromechanical systems : a joint IEEE and ASME publication on microstructures, microactuators, microsensors, and microsystems, 30(4):569-581.

Intracortical neural probes are a key enabling technology for acquiring high fidelity neural signals within the cortex. They are viewed as a crucial component of brain-computer interfaces (BCIs) in order to record electrical activities from neurons within the brain. Smaller, more flexible, polymer-based probes have been investigated for their potential to limit the acute and chronic neural tissue response. Conventional methods of patterning electrodes and connecting traces on a single supporting layer can limit the number of recording sites which can be defined, particularly when designing narrower probes. We present a novel strategy of increasing the number of recording sites without proportionally increasing the size of the probe by using a multilayer fabrication process to vertically layer recording traces on multiple Parylene support layers, allowing more recording traces to be defined on a smaller probe width. Using this approach, we are able to define 16 electrodes on 4 supporting layers (4 electrodes per layer), each with a 30 μm diameter recording window and 5 μm wide connecting trace defined by conventional LWUV lithography, on an 80 μm wide by 9 μm thick microprobe. Prior to in vitro and in vivo validation, the multilayer probes are electrically characterized via impedance spectroscopy and evaluating crosstalk between adjacent layers. Demonstration of acute in vitro recordings in a cerebral organoid model and in vivo recordings in a murine model indicate the probe's capability for single unit recordings. This work demonstrates the ability to fabricate smaller, more compliant neural probes without sacrificing electrode density.

RevDate: 2021-09-17

Wang X, Lu H, Shen X, et al (2021)

Prosthetic control system based on motor imagery.

Computer methods in biomechanics and biomedical engineering [Epub ahead of print].

A brain-computer interface (BCI) can be used for function replacement through the control of devices, such as prostheses, by identifying the subject's intent from brain activity. We process electroencephalography (EEG) signals related to motor imagery to improve the accuracy of intent classification. The original signals are decomposed into three layers based on db4 wavelet basis. The wavelet soft threshold denoising method is used to improve the signal-to-noise ratio. The sample entropy algorithm is used to extract the features of the signal after noise reduction and reconstruction. Combined with event-related synchronisation/desynchronisation (ERS/ERD) phenomenon, the sample entropy in the motor imagery time periods of C3, C4 and Cz is selected as the feature value. Feature vectors are then used as the input of three classifiers. From the evaluated classifiers, the backpropagation (BP) neural network provides the best EEG signal classification (93% accuracy). BP neural network is thus selected as the final classifier and used to design a prosthetic control system based on motor imagery. The classification results are wirelessly transmitted to control a prosthesis successfully via commands of hand opening, fist clenching, and external wrist rotation. Such functionality may allow amputees to complete simple activities of daily living. Thus, this study is valuable for subsequent developments in rehabilitation.

RevDate: 2021-09-21

Goering S, Brown T, E Klein (2021)

Neurotechnology ethics and relational agency.

Philosophy compass, 16(4):.

Novel neurotechnologies, like deep brain stimulation and brain-computer interface, offer great hope for treating, curing, and preventing disease, but raise important questions about effects these devices may have on human identity, authenticity, and autonomy. After briefly assessing recent narrative work in these areas, we show that agency is a phenomenon key to all three goods and highlight the ways in which neural devices can help to draw attention to the relational nature of our agency. Drawing on insights from disability theory, we argue that neural devices provide a kind of agential assistance, similar to that provided by caregivers, family, and others. As such, users and devices participate in a kind of co-agency. We conclude by suggesting the need for developing relational agency-competencies-skills for reflecting on the influence of devices on agency, for adapting to novel circumstances ushered in by devices, and for incorporating the feedback of loved ones and others about device effects on agency.

RevDate: 2021-09-14

Alfiero CJ, Brooks SJ, Bideganeta HM, et al (2021)

Protein Supplementation Does Not Improve Aerobic and Anaerobic Fitness in Collegiate Dancers Performing Cycling Based High Intensity Interval Training.

Journal of dance medicine & science : official publication of the International Association for Dance Medicine & Science [Epub ahead of print].

The effects of a 6-week cycling high-intensity interval training (HIIT) concurrently with protein supplementation on aerobic and anaerobic fitness and body composition in collegiate dancers was investigated. Eighteen participants enrolled in a collegiate dance program were matched into three groups: high-protein (HP; 90 g·d-1), moderate-protein (MP; 40 g·d-1), and control (C; 0 g·d-1). All participants performed a 6-week HIIT intervention. Participants completed a graded exercise test, Wingate anaerobic test (Wingate), and dual energy x-ray absorptiometry scan before and after the intervention. Peak heart rate (HRpeak), peak oxygen uptake (VOpeak), peak power output (PPO), lactate threshold (LT), and ventilatory thresholds 1 (VT1) and 2 (VT2) were assessed during the graded exercise test. Peak power output, mean power output (MPO), and fatigue index (FI) were assessed during the Wingate. Lean mass (LM), fat mass (FM), visceral adipose tissue, appendicular skeletal muscle mass, and appendicular skeletal muscle mass index were assessed during dual energy x-ray absorptiometry. Body composition index (BCI) was calculated from pre and post LM and FM. Habitual diet was recorded weekly. Significance was set at p ≤ 0.05. No significant differences in VO2peak and percent fat mass (%FM) were observed between groups prior to the intervention. Significant main effects for time were observed for HRpeak (p = 0.02), VO2peak (p < 0.001), PPO (p < 0.01), LT (p < 0.001), VT1 (p < 0.001), and VT2 (p < 0.001) during the graded exercise test, and PPO (p < 0.01) and FI (p < 0.01) during the Wingate. Significant main effects for time were observed for LM (kg; p = 0.01) and FM (kg; p < 0.01). Body composition index was improved for all groups, however, no significant differences by group were observed. No significant differences were observed between groups for the measured outcomes (p > 0.05). Therefore, there was no effect of protein supplementation in the short 6-week intervention. This cycling based HIIT routine increased physical fitness, optimized aesthetics, and was a simple addition to an existing collegiate dance curriculum.

RevDate: 2021-09-13

Zhang S, Yan X, Wang Y, et al (2021)

Modulation of brain states on fractal and oscillatory power of EEG in brain-computer interfaces.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Electroencephalogram (EEG) is an objective reflection of the brain activities, which provides potential possibilities for brain state estimation based on EEG characteristics. However, how to mine the effective EEG characteristics is still a distressing problem in brain state monitoring.

APPROACH: The phase-scrambled method was used to generate images with different noise levels. Images were encoded into a rapid serial visual presentation (RSVP) paradigm. N-back working memory method was employed to induce and assess fatigue state. The irregular-resampling auto-spectral analysis (IRASA) method was adopted to extract and parameterize (exponent and offset) the characteristics of EEG fractal components, which were analyzed in the four dimensions: fatigue, sustained attention, visual noise and experimental tasks.

RESULTS: The degree of fatigue and visual noise level had positive effects on exponent and offset in the prefrontal lobe, and the ability of sustained attention negatively affected exponent and offset. Compared with visual stimuli task, rest task induced even larger values of exponent and offset and statistically significant in the most cerebral cortex. In addition, the SSVEP amplitudes were negatively and positively affected by the degree of fatigue and noise levels, respectively.

SIGNIFICANCE: The conclusions of this study provide insights into the relationship between brain states and EEG characteristics. In addition, this study has the potential to provide objective methods for brain states monitoring and EEG modeling.

RevDate: 2021-09-24

Lin PJ, Jia T, Li C, et al (2021)

CNN-Based Prognosis of BCI Rehabilitation Using EEG From First Session BCI Training.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 29:1936-1943.

Stroke is a world-leading disease for causing disability. Brain-computer interaction (BCI) training has been proved to be a promising method in facilitating motor recovery. However, due to differences in each patient's neural-clinical profile, the potential of recovery for different patients can vary significantly by conducting BCI training, which remains a major problem in clinical rehabilitation practice. To address this issue, the objective of this study is to prognosticate the outcome of BCI training using motor state electroencephalographic (EEG) collected during the first session of BCI tasks, with the aim of prescribing BCI training accordingly. A Convolution Neural Network (CNN) based prognosis model was developed to predict the outcome of 11 stroke patients' recovery following a 2-week rehabilitation training with BCI. In our study, functional connectivity and power spectrum have been evaluated and applied as the inputs of CNN to regress patients' recovery rate. A saliency map was used to identify the correlation between EEG channels with the recovery outcome. The performance of our model was assessed using the leave-one-out cross-validation. Overall, the proposed model predicted patients' recovery with R2 0.98 and MSE 0.89. According to the saliency map, the highest functional connectivity occurred in Fp2/Fpz-AF8, Fp2/F4/F8-P3, P1/PO7-PO5 and AF3-AF4. Our results demonstrated that deep learning method has the potential to predict the recovery rate of BCI training, which contributes to guiding individualized prescription in the early stage of clinical rehabilitation.

RevDate: 2021-09-13

Glikmann-Johnston Y, Mercieca EC, Carmichael AM, et al (2021)

Hippocampal and striatal volumes correlate with spatial memory impairment in Huntington's disease.

Journal of neuroscience research [Epub ahead of print].

Spatial memory impairments are observed in people with Huntington's disease (HD), however, the domain of spatial memory has received little focus when characterizing the cognitive phenotype of HD. Spatial memory is traditionally thought to be a hippocampal-dependent function, while the neuropathology of HD centers on the striatum. Alongside spatial memory deficits in HD, recent neurocognitive theories suggest that a larger brain network is involved, including the striatum. We examined the relationship between hippocampal and striatal volumes and spatial memory in 36 HD gene expansion carriers, including premanifest (n = 24) and early manifest HD (n = 12), and 32 matched healthy controls. We assessed spatial memory with Paired Associates Learning, Rey-Osterrieth Complex Figure Test, and the Virtual House task, which assesses three components of spatial memory: navigation, object location, and plan drawing. Caudate nucleus, putamen, and hippocampal volumes were manually segmented on T1-weighted MR images. As expected, caudate nucleus and putamen volumes were significantly smaller in the HD group compared to controls, with manifest HD having more severe atrophy than the premanifest HD group. Hippocampal volumes did not differ significantly between HD and control groups. Nonetheless, on average, the HD group performed significantly worse than controls across all spatial memory tasks. The spatial memory components of object location and recall of figural and topographical drawings were associated with striatal and hippocampal volumes in the HD cohort. We provide a case to include spatial memory impairments in the cognitive phenotype of HD, and extend the neurocognitive picture of HD beyond its primary pathology within the striatum.

RevDate: 2021-09-14

Guan S, Li J, Wang F, et al (2021)

Discriminating three motor imagery states of the same joint for brain-computer interface.

PeerJ, 9:e12027.

The classification of electroencephalography (EEG) induced by the same joint is one of the major challenges for brain-computer interface (BCI) systems. In this paper, we propose a new framework, which includes two parts, feature extraction and classification. Based on local mean decomposition (LMD), cloud model, and common spatial pattern (CSP), a feature extraction method called LMD-CSP is proposed to extract distinguishable features. In order to improve the classification results multi-objective grey wolf optimization twin support vector machine (MOGWO-TWSVM) is applied to discriminate the extracted features. We evaluated the performance of the proposed framework on our laboratory data sets with three motor imagery (MI) tasks of the same joint (shoulder abduction, extension, and flexion), and the average classification accuracy was 91.27%. Further comparison with several widely used methods showed that the proposed method had better performance in feature extraction and pattern classification. Overall, this study can be used for developing high-performance BCI systems, enabling individuals to control external devices intuitively and naturally.

RevDate: 2021-09-14

Sun B, W Zhao (2021)

Compressed Sensing of Extracellular Neurophysiology Signals: A Review.

Frontiers in neuroscience, 15:682063.

This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless streaming of massively parallel neural recording channels in next-generation neural interface technologies. The main objective is to provide a timely retrospective on applying the CS theory to the extracellular brain signals in the past decade. We will present a comprehensive review on the CS-based neural recording system architecture, the CS encoder hardware exploration and implementation, the sparse representation of neural signals, and the signal reconstruction algorithms. Deep learning-based CS methods are also discussed and compared with the traditional CS-based approaches. We will also extend our discussion to cover the technical challenges and prospects in this emerging field.

RevDate: 2021-09-12

Rubio-Tomás T (2021)

Novel insights into SMYD2 and SMYD3 inhibitors: from potential anti-tumoural therapy to a variety of new applications.

Molecular biology reports [Epub ahead of print].

The revelance of the epigenetic regulation of cancer led to the design and testing of many drugs targeting epigenetic modifiers. The Su(Var)3-9, Enhancer-of-zeste and Trithorax (SET) and myeloid, Nervy, and DEAF-1 (MYND) domain-containing protein 2 (SMYD2) and 3 (SMYD3) are methyltransferases which act on histone and non-histone proteins to promote tumorigenesis in many cancer types. In addition to their oncogenic roles, SMYD2 and SMYD3 are involved in many other physiopathological conditions. In this review we will focus on the advances made in the last five years in the field of pharmacology regarding drugs targeting SMYD2 (such as LLY-507 or AZ505) and SMYD3 (such as BCI-121 or EPZ031686) and their potential cellular and molecular mechanisms of action and application in anti-tumoural therapy and/or against other diseases.

RevDate: 2021-09-11

Hou Y, Chen T, Lun X, et al (2021)

A novel method for classification of multi-class motor imagery tasks based on feature fusion.

Neuroscience research pii:S0168-0102(21)00204-2 [Epub ahead of print].

Motor Imagery based Brain computer interface (MI-BCI) has the advantage of strong independence that can rely on the spontaneous brain activity of the user to operate external devices. However, MI-BCI still has the problem of poor control effect, which requires more effective feature extraction algorithms and classification methods to extract distinctly separable features from electroencephalogram (EEG) signals. This paper proposes a novel framework based on Bispectrum, Entropy and common spatial pattern (BECSP). Here we use three methods of bispectrum in higher order spectra, entropy and CSP to extract MI-EEG signal features, and then select the most contributing features through tree-based feature selection algorithm. By comparing the classification results of SVM, Random Forest, Naive Bayes, LDA, KNN, Xgboost and Adaboost, we finally decide to use the SVM algorithm based on RBF kernel function which obtained the best result among them for classification. The proposed method is applied to the BCI competition IV data set 2a and BCI competition III data set IVa. On data set 2a, the highest accuracy on the evaluation data set reaches 85%. The experiment on data set IVa can also achieve good results. Compared with other algorithms that use the same data set, the performance of our algorithm has also been improved.

RevDate: 2021-09-20

Sato Y, Kondo T, Shinozaki M, et al (2021)

Markerless analysis of hindlimb kinematics in spinal cord-injured mice through deep learning.

Neuroscience research pii:S0168-0102(21)00203-0 [Epub ahead of print].

Rodent models are commonly used to understand the underlying mechanisms of spinal cord injury (SCI). Kinematic analysis, an important technique to measure dysfunction of locomotion after SCI, is generally based on the capture of physical markers placed on bony landmarks. However, marker-based studies face significant experimental hurdles such as labor-intensive manual joint tracking, alteration of natural gait by markers, and skin error from soft tissue movement on the knee joint. Although the pose estimation strategy using deep neural networks can solve some of these issues, it remains unclear whether this method is adaptive to SCI mice with abnormal gait. In the present study, we developed a deep learning based markerless method of 2D kinematic analysis to automatically track joint positions. We found that a relatively small number (< 200) of manually labeled video frames was sufficient to train the network to extract trajectories. The mean test error was on average 3.43 pixels in intact mice and 3.95 pixels in SCI mice, which is comparable to the manual tracking error (3.15 pixels, less than 1 mm). Thereafter, we extracted 30 gait kinematic parameters and found that certain parameters such as step height and maximal hip joint amplitude distinguished intact and SCI locomotion.

RevDate: 2021-09-10

Si X, Li S, Xiang S, et al (2021)

Imagined speech increases the hemodynamic response and functional connectivity of the dorsal motor cortex.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Decoding imagined speech from brain signals could provide a more natural, user-friendly way for developing the next generation of the brain-computer interface (BCI). With the advantages of non-invasive, portable, relatively high spatial resolution and insensitivity to motion artifacts, the functional near-infrared spectroscopy (fNIRS) shows great potential for developing the non-invasive speech BCI. However, there is a lack of fNIRS evidence in uncovering the neural mechanism of imagined speech. Our goal is to investigate the specific brain regions and the corresponding cortico-cortical functional connectivity features during imagined speech with fNIRS.

APPROACH: fNIRS signals were recorded from thirteen subjects' bilateral motor and prefrontal cortex during overtly and covertly repeating words. Cortical activation was determined through the mean oxygen-hemoglobin concentration changes, and functional connectivity was calculated by Pearson's correlation coefficient.

MAIN RESULTS: (1) The bilateral dorsal motor cortex was significantly activated during the covert speech, whereas the bilateral ventral motor cortex was significantly activated during the overt speech. (2) As a subregion of the motor cortex, sensorimotor cortex (SMC) showed a dominant dorsal response to covert speech condition, whereas a dominant ventral response to overt speech condition. (3) Broca's area was deactivated during the covert speech but activated during the overt speech. (4) Compared to overt speech, dorsal SMC-related functional connections were enhanced during the covert speech.

SIGNIFICANCE: We provide fNIRS evidence for the involvement of dSMC in speech imagery. dSMC is the speech imagery network's key hub and is probably involved in the sensorimotor information processing during the covert speech. This study could inspire the BCI community to focus on the potential contribution of dSMC during speech imagery.

RevDate: 2021-09-22

Zhang Y, Le S, Li H, et al (2021)

MRI magnetic compatible electrical neural interface: From materials to application.

Biosensors & bioelectronics, 194:113592 pii:S0956-5663(21)00629-1 [Epub ahead of print].

Neural electrical interfaces are important tools for local neural stimulation and recording, which potentially have wide application in the diagnosis and treatment of neural diseases, as well as in the transmission of neural activity for brain-computer interface (BCI) systems. At the same time, magnetic resonance imaging (MRI) is one of the effective and non-invasive techniques for recording whole-brain signals, providing details of brain structures and also activation pattern maps. Simultaneous recording of extracellular neural signals and MRI combines two expressions of the same neural activity and is believed to be of great importance for the understanding of brain function. However, this combination makes requests on the magnetic and electronic performance of neural interface devices. MRI-compatibility refers here to a technological approach to simultaneous MRI and electrode recording or stimulation without artifacts in imaging. Trade-offs between materials magnetic susceptibility selection and electrical function should be considered. Herein, prominent trends in selecting materials of suitable magnetic properties are analyzed and material design, function and application of neural interfaces are outlined together with the remaining challenge to fabricate MRI-compatible neural interface.

RevDate: 2021-09-26

Zhang XN, Meng QH, Zeng M, et al (2021)

Decoding olfactory EEG signals for different odor stimuli identification using wavelet-spatial domain feature.

Journal of neuroscience methods, 363:109355.

BACKGROUND: Decoding olfactory-induced electroencephalography (olfactory EEG) signals has gained significant attention in recent years, owing to its potential applications in several fields, such as disease diagnosis, multimedia applications, and brain-computer interaction (BCI). Extracting discriminative features from olfactory EEG signals with low spatial resolution and poor signal-to-noise ratio is vital but challenging for improving decoding accuracy.

NEW METHODS: By combining discrete wavelet transform (DWT) with one-versus-rest common spatial pattern (OVR-CSP), we develop a novel feature, named wavelet-spatial domain feature (WSDF), to decode the olfactory EEG signals. First, DWT is employed on EEG signals for multilevel wavelet decomposition. Next, the DWT coefficients obtained at a specific level are subjected to OVR-CSP for spatial filtering. Correspondingly, the variance is extracted to generate a discriminative feature set, labeled as WSDF.

RESULTS: To verify the effectiveness of WSDF, a classification of olfactory EEG signals was conducted on two data sets, i.e., a public EEG dataset 'Odor Pleasantness Perception Dataset (OPPD)', and a self-collected dataset, by using support vector machine (SVM) trained based on different cross-validation methods. Experimental results showed that on OPPD dataset, the proposed method achieved a best average accuracy of 100% and 94.47% for the eyes-open and eyes-closed conditions, respectively. Moreover, on our own dataset, the proposed method gave a highest average accuracy of 99.50%.

Compared with a wide range of EEG features and existing works on the same dataset, our WSDF yielded superior classification performance.

CONCLUSIONS: The proposed WSDF is a promising candidate for decoding olfactory EEG signals.

RevDate: 2021-09-10

Bisht A, Singh P, Kaur C, et al (2021)

Progress and Challenges in Physiological Artifacts' Detection in Electroencephalographic Readings.

Current medical imaging pii:CMIR-EPUB-117769 [Epub ahead of print].

BACKGROUND: Electroencephalographic (EEG) recordings are used to trace neural activity within the cortex to study brain functioning over time.

INTRODUCTION: During data acquisition, the unequivocal way to reduce artifact is to avoid artifact stimulating events. Though there are certain artifacts that make this task challenging due to their association with the internal human mechanism, in the human-computer interface, these physiological artifacts are of great assistance and act as a command signal for controlling a device or an application (communication). That is why pre-processing of electroencephalographic readings has been a progressive area of exploration, as none of the published work can be viewed as a benchmark for constructive artifact handling.

METHOD: This review offers a comprehensive insight into state of the art physiological artifact removal techniques listed so far. The study commences from the single-stage traditional techniques to the multistage techniques, examining the pros and cons of each discussed technique. Also, this review paper gives a general idea of various datasets available and briefs the topical trend in EEG signal processing.

RESULT: Comparing the state of the art techniques with hybrid ones on the basis of performance and computational complexity, it has been observed that the single-channel techniques save computational time but lack in effective artifact removal especially physiological artifacts. On the other hand, hybrid techniques merge the essential characteristics resulting in increased performance, but time consumption and complexity remain an issue.

CONCLUSION: Considering the high probability of the presence of multiple artifacts in EEG channels, a trade-off between performance, time and computational complexity is the only key for effective processing of artifacts in the time ahead. This paper is anticipated to facilitate upcoming researchers in enriching the contemporary artifact handling techniques to mitigate the expert's burden.

RevDate: 2021-09-14
CmpDate: 2021-09-13

Kim S, Lee S, Kang H, et al (2021)

P300 Brain-Computer Interface-Based Drone Control in Virtual and Augmented Reality.

Sensors (Basel, Switzerland), 21(17):.

Since the emergence of head-mounted displays (HMDs), researchers have attempted to introduce virtual and augmented reality (VR, AR) in brain-computer interface (BCI) studies. However, there is a lack of studies that incorporate both AR and VR to compare the performance in the two environments. Therefore, it is necessary to develop a BCI application that can be used in both VR and AR to allow BCI performance to be compared in the two environments. In this study, we developed an opensource-based drone control application using P300-based BCI, which can be used in both VR and AR. Twenty healthy subjects participated in the experiment with this application. They were asked to control the drone in two environments and filled out questionnaires before and after the experiment. We found no significant (p > 0.05) difference in online performance (classification accuracy and amplitude/latency of P300 component) and user experience (satisfaction about time length, program, environment, interest, difficulty, immersion, and feeling of self-control) between VR and AR. This indicates that the P300 BCI paradigm is relatively reliable and may work well in various situations.

RevDate: 2021-09-14
CmpDate: 2021-09-13

Mridha MF, Das SC, Kabir MM, et al (2021)

Brain-Computer Interface: Advancement and Challenges.

Sensors (Basel, Switzerland), 21(17):.

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

RevDate: 2021-09-14
CmpDate: 2021-09-13

Appriou A, Pillette L, Trocellier D, et al (2021)

BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification.

Sensors (Basel, Switzerland), 21(17):.

Research on brain-computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.

RevDate: 2021-09-13

Monini S, Filippi C, De Luca A, et al (2021)

On the Effect of Bimodal Rehabilitation in Asymmetric Hearing Loss.

Journal of clinical medicine, 10(17):.

BACKGROUND: Bone conductive implants (BCI) have been reported to provide greater beneficial effects for the auditory and perceptual functions of the contralateral ear in patients presenting with asymmetric hearing loss (AHL) compared to those with single-sided deafness (SSD). The aim of the study was to assess the effects of wearing a conventional hearing aid in the contralateral ear on BCI in terms of an improved overall auditory performance.

METHODS: eleven AHL subjects wearing a BCI in their worse hearing ear underwent an auditory evaluation by pure tone and speech audiometry in free field. This study group was obtained by adding to the AHL patients those SSD subjects that, during the follow-up, showed deterioration of the hearing threshold of the contralateral ear, thus presenting with the features of AHL. Four different conditions were tested and compared: unaided, with BCI only, with contralateral hearing aid (CHA) only and with BCI combined with CHA.

RESULTS: all of the prosthetic conditions caused a significant improvement with respect to the unaided condition. When a CHA was adopted, its combination with the BCI showed significantly better auditory performances than those achieved with the BCI only.

CONCLUSIONS: the present study suggests the beneficial role of a CHA in BCI-implanted AHL subjects in terms of overall auditory performance.

RevDate: 2021-09-09

Nason SR, Mender MJ, Vaskov AK, et al (2021)

Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface.

Neuron pii:S0896-6273(21)00604-8 [Epub ahead of print].

Modern brain-machine interfaces can return function to people with paralysis, but current upper extremity brain-machine interfaces are unable to reproduce control of individuated finger movements. Here, for the first time, we present a real-time, high-speed, linear brain-machine interface in nonhuman primates that utilizes intracortical neural signals to bridge this gap. We created a non-prehensile task that systematically individuates two finger groups, the index finger and the middle-ring-small fingers combined. During online brain control, the ReFIT Kalman filter could predict individuated finger group movements with high performance. Next, training ridge regression decoders with individual movements was sufficient to predict untrained combined movements and vice versa. Finally, we compared the postural and movement tuning of finger-related cortical activity to find that individual cortical units simultaneously encode multiple behavioral dimensions. Our results suggest that linear decoders may be sufficient for brain-machine interfaces to execute high-dimensional tasks with the performance levels required for naturalistic neural prostheses.

RevDate: 2021-09-10

Singh AK, Sahonero-Alvarez G, Mahmud M, et al (2021)

Towards Bridging the Gap Between Computational Intelligence and Neuroscience in Brain-Computer Interfaces With a Common Description of Systems and Data.

Frontiers in neuroinformatics, 15:699840.

RevDate: 2021-09-08

Guney OB, Oblokulov M, H Ozkan (2021)

A Deep Neural Network for SSVEP-based Brain-Computer Interfaces.

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

OBJECTIVE: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture.

METHOD: The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities.

RESULTS: Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The code is available for reproducibility at https://github.com/osmanberke/Deep-SSVEP-BCI.

CONCLUSION: The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets.

SIGNIFICANCE: Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.

RevDate: 2021-09-24

Chen R, Xu G, Zheng Y, et al (2021)

Waveform feature extraction and signal recovery in single-channel TVEP based on Fitzhugh-Nagumo stochastic resonance.

Journal of neural engineering, 18(5):.

Objective. Transient visual evoked potential (TVEP) can reflect the condition of the visual pathway and has been widely used in brain-computer interface. TVEP signals are typically obtained by averaging the time-locked brain responses across dozens or even hundreds of stimulations, in order to remove different kinds of interferences. However, this procedure increases the time needed to detect the brain status in realistic applications. Meanwhile, long repeated stimuli can vary the evoked potentials and discomfort the subjects. Therefore, a novel unsupervised framework was developed in this study to realize the fast extraction of single-channel TVEP signals with a high signal-to-noise ratio.Approach.Using the principle of nonlinear aperiodic FitzHugh-Nagumo (FHN) model, a fast extraction and signal restoration technology of TVEP waveform based on FHN stochastic resonance is proposed to achieve high-quality acquisition of signal features with less average times.Results:A synergistic effect produced by noise, aperiodic signal and nonlinear system can force the energy of noise to be transferred into TVEP and hence amplifying the useful P100 feature while suppressing multi-scale noise.Significance. Compared with the conventional average and average-singular spectrum analysis-independent component analysis(average-SSA-ICA) method, the average-FHN method has a shorter stimulation time which can greatly improve the comfort of patients in clinical TVEP detection and a better performance of TVEP waveform i.e. a higher accuracy of P100 latency. The FHN recovery method is not only highly correlated with the original signal, but also can better highlight the P100 amplitude, which has high clinical application value.

RevDate: 2021-09-24

Zhang Y, Cai H, Nie L, et al (2021)

An end-to-end 3D convolutional neural network for decoding attentive mental state.

Neural networks : the official journal of the International Neural Network Society, 144:129-137 pii:S0893-6080(21)00324-5 [Epub ahead of print].

The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation for the electroencephalogram (EEG) data, which could preserve the temporal information and maintain the spatial topological characteristics. Inspired by the deep learning (DL) methods in the research of brain-computer interface (BCI) field, a 3D representation of EEG signal was introduced into attention detection task, and a 3D convolutional neural network model with cascade and parallel convolution operations was proposed. The model utilized three cascade blocks, each consisting of two parallel 3D convolution branches, to simultaneously extract the multi-scale features. Evaluated on a public dataset containing twenty-six subjects, the proposed model achieved better performance compared with the baseline methods under the intra-subject, inter-subject and subject-adaptive classification scenarios. This study demonstrated the promising potential of the 3D CNN model for detecting attentive mental state.

RevDate: 2021-09-24

Ali A, Tariq H, Abbas S, et al (2021)

Draft genome sequence of a multidrug-resistant novel candidate Pseudomonas sp. NCCP-436T isolated from faeces of a bovine host in Pakistan.

Journal of global antimicrobial resistance, 27:91-94 pii:S2213-7165(21)00203-4 [Epub ahead of print].

OBJECTIVES: Here we describe the first draft genome analysis of a CRISPR-carrying, multidrug-resistant, candidate novel Pseudomonas sp. NCCP-436T isolated from faeces of a neonatal diarrhoeic calf.

METHODS: The genome of strain NCCP-436T was sequenced using an Illumina NovaSeq PE150 platform and analysed using various bioinformatic tools. The virulence factors and resistome were identified using PATRIC and CARD servers, while CGView Server was used to construct a circular genome map. Antimicrobial susceptibility was determined by the disk diffusion technique.

RESULTS: The draft genome of strain NCCP-436T contains 43 contigs with a total genome size of 3,683,517 bp (61.4% GC content). There are 3,452 predicted genes, including 60 tRNAs, 7 rRNAs and 12 sRNAs. CRISPR analysis revealed two CRISPR arrays with lengths of 1103 bp and 867 bp. Strain NCCP-436T was highly resistant to fluoroquinolone, β-lactam, cephalosporin, aminoglycoside, penicillin, rifamycin, macrolide, glycopeptide, trimethoprim/sulfonamide and tetracycline antibiotic classes. Additionally, 22 antibiotic resistance genes, 313 virulence genes and 253 pathogen-host interactor genes were predicted. Comparison of the average nucleotide identity and digital DNA-DNA hybridisation values with the closely-related strain Pseudomonas khazarica (TBZ2) was found to be 82.08% and 34.90%, respectively, illustrating strain NCCP-436T as a potentially new species of Pseudomonas.

CONCLUSION: Substantial number of antibiotic resistance and virulence genes and homology with human pathogens were predicted, exposing the pathogenic and zoonotic potential of strain NCCP-436T to public health. These findings may be used to better understand the genomic epidemiological features and drug resistance mechanisms of pathogenic Pseudomonas spp. in Pakistan.

RevDate: 2021-09-08

Shan B, Pu Y, Chen B, et al (2021)

New Technologies' Commercialization: The Roles of the Leader's Emotion and Incubation Support.

Frontiers in psychology, 12:710122.

New technologies, such as brain-computer interfaces technology, advanced artificial intelligence, cloud computing, and virtual reality technology, have a strong influence on our daily activities. The application and commercialization of these technologies are prevailing globally, such as distance education, health monitoring, smart home devices, and robots. However, we still know little about the roles of individual emotion and the external environment on the commercialization of these new technologies. Therefore, we focus on the emotional factor of the leader, which is their passion for work, and discuss its effect on technology commercialization. We also analyzed the moderating role of incubation support in the relationship between the leader's emotion and technology commercialization. The results contribute to the application of emotion in improving the commercialization of new technologies.

RevDate: 2021-09-08

Huang X, Xu Y, Hua J, et al (2021)

A Review on Signal Processing Approaches to Reduce Calibration Time in EEG-Based Brain-Computer Interface.

Frontiers in neuroscience, 15:733546.

In an electroencephalogram- (EEG-) based brain-computer interface (BCI), a subject can directly communicate with an electronic device using his EEG signals in a safe and convenient way. However, the sensitivity to noise/artifact and the non-stationarity of EEG signals result in high inter-subject/session variability. Therefore, each subject usually spends long and tedious calibration time in building a subject-specific classifier. To solve this problem, we review existing signal processing approaches, including transfer learning (TL), semi-supervised learning (SSL), and a combination of TL and SSL. Cross-subject TL can transfer amounts of labeled samples from different source subjects for the target subject. Moreover, Cross-session/task/device TL can reduce the calibration time of the subject for the target session, task, or device by importing the labeled samples from the source sessions, tasks, or devices. SSL simultaneously utilizes the labeled and unlabeled samples from the target subject. The combination of TL and SSL can take advantage of each other. For each kind of signal processing approaches, we introduce their concepts and representative methods. The experimental results show that TL, SSL, and their combination can obtain good classification performance by effectively utilizing the samples available. In the end, we draw a conclusion and point to research directions in the future.

RevDate: 2021-09-08

Shupe LE, Miles FP, Jones G, et al (2021)

Neurochip3: An Autonomous Multichannel Bidirectional Brain-Computer Interface for Closed-Loop Activity-Dependent Stimulation.

Frontiers in neuroscience, 15:718465.

Toward addressing many neuroprosthetic applications, the Neurochip3 (NC3) is a multichannel bidirectional brain-computer interface that operates autonomously and can support closed-loop activity-dependent stimulation. It consists of four circuit boards populated with off-the-shelf components and is sufficiently compact to be carried on the head of a non-human primate (NHP). NC3 has six main components: (1) an analog front-end with an Intan biophysical signal amplifier (16 differential or 32 single-ended channels) and a 3-axis accelerometer, (2) a digital control system comprised of a Cyclone V FPGA and Atmel SAM4 MCU, (3) a micro SD Card for 128 GB or more storage, (4) a 6-channel differential stimulator with ±60 V compliance, (5) a rechargeable battery pack supporting autonomous operation for up to 24 h and, (6) infrared transceiver and serial ports for communication. The NC3 and earlier versions have been successfully deployed in many closed-loop operations to induce synaptic plasticity and bridge lost biological connections, as well as deliver activity-dependent intracranial reinforcement. These paradigms to strengthen or replace impaired connections have many applications in neuroprosthetics and neurorehabilitation.

RevDate: 2021-09-07

Douibi K, Le Bars S, Lemontey A, et al (2021)

Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications.

Frontiers in human neuroscience, 15:705064.

In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0.

RevDate: 2021-09-07

Zhou Q, Lin J, Yao L, et al (2021)

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects.

Frontiers in human neuroscience, 15:701091.

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

RevDate: 2021-09-07

Bouton C, Bhagat N, Chandrasekaran S, et al (2021)

Decoding Neural Activity in Sulcal and White Matter Areas of the Brain to Accurately Predict Individual Finger Movement and Tactile Stimuli of the Human Hand.

Frontiers in neuroscience, 15:699631.

Millions of people worldwide suffer motor or sensory impairment due to stroke, spinal cord injury, multiple sclerosis, traumatic brain injury, diabetes, and motor neuron diseases such as ALS (amyotrophic lateral sclerosis). A brain-computer interface (BCI), which links the brain directly to a computer, offers a new way to study the brain and potentially restore impairments in patients living with these debilitating conditions. One of the challenges currently facing BCI technology, however, is to minimize surgical risk while maintaining efficacy. Minimally invasive techniques, such as stereoelectroencephalography (SEEG) have become more widely used in clinical applications in epilepsy patients since they can lead to fewer complications. SEEG depth electrodes also give access to sulcal and white matter areas of the brain but have not been widely studied in brain-computer interfaces. Here we show the first demonstration of decoding sulcal and subcortical activity related to both movement and tactile sensation in the human hand. Furthermore, we have compared decoding performance in SEEG-based depth recordings versus those obtained with electrocorticography electrodes (ECoG) placed on gyri. Initial poor decoding performance and the observation that most neural modulation patterns varied in amplitude trial-to-trial and were transient (significantly shorter than the sustained finger movements studied), led to the development of a feature selection method based on a repeatability metric using temporal correlation. An algorithm based on temporal correlation was developed to isolate features that consistently repeated (required for accurate decoding) and possessed information content related to movement or touch-related stimuli. We subsequently used these features, along with deep learning methods, to automatically classify various motor and sensory events for individual fingers with high accuracy. Repeating features were found in sulcal, gyral, and white matter areas and were predominantly phasic or phasic-tonic across a wide frequency range for both HD (high density) ECoG and SEEG recordings. These findings motivated the use of long short-term memory (LSTM) recurrent neural networks (RNNs) which are well-suited to handling transient input features. Combining temporal correlation-based feature selection with LSTM yielded decoding accuracies of up to 92.04 ± 1.51% for hand movements, up to 91.69 ± 0.49% for individual finger movements, and up to 83.49 ± 0.72% for focal tactile stimuli to individual finger pads while using a relatively small number of SEEG electrodes. These findings may lead to a new class of minimally invasive brain-computer interface systems in the future, increasing its applicability to a wide variety of conditions.

RevDate: 2021-09-07

Kang JH, Youn J, Kim SH, et al (2021)

Effects of Frontal Theta Rhythms in a Prior Resting State on the Subsequent Motor Imagery Brain-Computer Interface Performance.

Frontiers in neuroscience, 15:663101.

Dealing with subjects who are unable to attain a proper level of performance, that is, those with brain-computer interface (BCI) illiteracy or BCI inefficients, is still a major issue in human electroencephalography (EEG) BCI systems. The most suitable approach to address this issue is to analyze the EEG signals of individual subjects independently recorded before the main BCI tasks, to evaluate their performance on these tasks. This study mainly focused on non-linear analyses and deep learning techniques to investigate the significant relationship between the intrinsic characteristics of a prior idle resting state and the subsequent BCI performance. To achieve this main objective, a public EEG motor/movement imagery dataset that constituted two individual EEG signals recorded from an idle resting state and a motor imagery BCI task was used in this study. For the EEG processing in the prior resting state, spectral analysis but also non-linear analyses, such as sample entropy, permutation entropy, and recurrent quantification analyses (RQA), were performed to obtain individual groups of EEG features to represent intrinsic EEG characteristics in the subject. For the EEG signals in the BCI tasks, four individual decoding methods, as a filter-bank common spatial pattern-based classifier and three types of convolution neural network-based classifiers, quantified the subsequent BCI performance in the subject. Statistical linear regression and ANOVA with post hoc analyses verified the significant relationship between non-linear EEG features in the prior resting state and three types of BCI performance as low-, intermediate-, and high-performance groups that were statistically discriminated by the subsequent BCI performance. As a result, we found that the frontal theta rhythm ranging from 4 to 8 Hz during the eyes open condition was highly associated with the subsequent BCI performance. The RQA findings that higher determinism and lower mean recurrent time were mainly observed in higher-performance groups indicate that more regular and stable properties in the EEG signals over the frontal regions during the prior resting state would provide a critical clue to assess an individual BCI ability in the following motor imagery task.

RevDate: 2021-09-17
CmpDate: 2021-09-17

Senathirajah Y, Hribar M, Section Editors of the IMIA Yearbook Section on Human Factors and Organizational Issues (2021)

Human Factors and Organizational Issues Section Synopsis IMIA Yearbook 2021.

Yearbook of medical informatics, 30(1):100-104.

OBJECTIVE: To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2020.

METHODS: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2020 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 1,562 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams.

RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces. This year three papers were clearly outstanding and help advance in the field. They provide examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors. Emerging themes included several which discussed physician burnout, mobile health, and health equity. Those concerning the Corona Virus Disease 2019 (Covid-19) were included as part of that section.

CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.

RevDate: 2021-09-03

Erdoğan SB, Yükselen G, Yegül MM, et al (2021)

Identification of impulsive adolescents with a functional near infrared spectroscopy (fNIRS) based decision support system.

Journal of neural engineering [Epub ahead of print].

BACKGROUND: The gold standard for diagnosing impulsivity relies on clinical interviews, behavioral questionnaires and rating scales which are highly subjective.

OBJECTIVE: The aim of this study was to develop a functional near infrared spectroscopy (fNIRS) based classification approach for correct identification of impulsive adolescents. Taking into account the multifaceted nature of impulsivity, we propose that combining informative features from clinical, behavioral and neurophysiological domains might better elucidate the neurobiological distinction underlying symptoms of impulsivity.

APPROACH: Hemodynamic and behavioral information was collected from 38 impulsive adolescents and from 33 non-impulsive adolescents during a Stroop task with concurrent fNIRS recordings. Connectivity-based features were computed from the hemodynamic signals and a neural efficiency metric was computed by fusing the behavioral and connectivity-based features. We tested the efficacy of two commonly used supervised machine-learning methods, namely the support vector machines (SVM) and artificial neural networks (ANN) in discriminating impulsive adolescents from their non-impulsive peers when trained with multi-domain features. Wrapper method was adapted to identify the informative biomarkers in each domain. Classification accuracies of each algorithm were computed after 10 runs of a 10-fold cross-validation procedure, conducted for 7 different combinations of the 3-domain feature set.

MAIN RESULTS: Both SVM and ANN achieved diagnostic accuracies above 90% when trained with Wrapper-selected clinical, behavioral and fNIRS derived features. SVM performed significantly higher than ANN in terms of the accuracy metric (%92.2 and %90.16 respectively, p=0.005).

SIGNIFICANCE: Preliminary findings show the feasibility and applicability of both machine-learning based methods for correct identification of impulsive adolescents when trained with multi-domain data involving clinical interviews, fNIRS based biomarkers and neuropsychiatric test measures. The proposed automated classification approach holds promise for assisting the clinical practice of diagnosing impulsivity and other psychiatric disorders. Our results also pave the path for a computer-aided diagnosis perspective for rating the severity of impulsivity.

RevDate: 2021-09-24

Haddix C, Al-Bakri AF, S Sunderam (2021)

Prediction of isometric handgrip force from graded event-related desynchronization of the sensorimotor rhythm.

Journal of neural engineering, 18(5):.

Objective. Brain-computer interfaces (BCIs) show promise as a direct line of communication between the brain and the outside world that could benefit those with impaired motor function. But the commands available for BCI operation are often limited by the ability of the decoder to differentiate between the many distinct motor or cognitive tasks that can be visualized or attempted. Simple binary command signals (e.g. right hand at rest versus movement) are therefore used due to their ability to produce large observable differences in neural recordings. At the same time, frequent command switching can impose greater demands on the subject's focus and takes time to learn. Here, we attempt to decode the degree of effort in a specific movement task to produce a graded and more flexible command signal.Approach.Fourteen healthy human subjects (nine male, five female) responded to visual cues by squeezing a hand dynamometer to different levels of predetermined force, guided by continuous visual feedback, while the electroencephalogram (EEG) and grip force were monitored. Movement-related EEG features were extracted and modeled to predict exerted force.Main results.We found that event-related desynchronization (ERD) of the 8-30 Hz mu-beta sensorimotor rhythm of the EEG is separable for different degrees of motor effort. Upon four-fold cross-validation, linear classifiers were found to predict grip force from an ERD vector with mean accuracies across subjects of 53% and 55% for the dominant and non-dominant hand, respectively. ERD amplitude increased with target force but appeared to pass through a trough that hinted at non-monotonic behavior.Significance.Our results suggest that modeling and interactive feedback based on the intended level of motor effort is feasible. The observed ERD trends suggest that different mechanisms may govern intermediate versus low and high degrees of motor effort. This may have utility in rehabilitative protocols for motor impairments.

RevDate: 2021-09-17

Al-Qazzaz NK, Alyasseri ZAA, Abdulkareem KH, et al (2021)

EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation.

Computers in biology and medicine, 137:104799 pii:S0010-4825(21)00593-X [Epub ahead of print].

Stroke is the second foremost cause of death worldwide and is one of the most common causes of disability. Several approaches have been proposed to manage stroke patient rehabilitation such as robotic devices and virtual reality systems, and researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. Therefore, the most challenging tasks with BCI applications involve identifying the best technique(s) that can reveal the neuron stimulus information from the patients' brains and extracting the most effective features from these signals as well. Accordingly, the main novelty of this paper is twofold: propose a new feature fusion method for motor imagery (MI)-based BCI and develop an automatic MI framework to detect the changes pre- and post-rehabilitation. This study investigated the electroencephalography (EEG) dataset from post-stroke patients with upper extremity hemiparesis. All patients performed 25 MI-based BCI sessions with follow up assessment visits to examine the functional changes before and after EEG neurorehabilitation. In the first stage, conventional filters and automatic independent component analysis with wavelet transform (AICA-WT) denoising technique were used. Next, attributes from time, entropy and frequency domains were computed, and the effective features were combined into time-entropy-frequency (TEF) attributes. Consequently, the AICA-WT and the TEF fusion set were utilised to develop an AICA-WT-TEF framework. Then, support vector machine (SVM), k-nearest neighbours (kNN) and random forest (RF) classification technique were tested for MI-based BCI rehabilitation. The proposed AICA-WT-TEF framework with RF classifier achieves the best results compared with other classifiers. Finally, the proposed framework and feature fusion set achieve a significant performance in terms of accuracy measures compared to the state-of-the-art. Therefore, the proposed methods could be crucial for improving the process of automatic MI rehabilitation and are recommended for implementation in real-time applications.

RevDate: 2021-09-23

Zhang B, Yuan P, Xu G, et al (2021)

DUSP6 expression is associated with osteoporosis through the regulation of osteoclast differentiation via ERK2/Smad2 signaling.

Cell death & disease, 12(9):825.

Osteoporosis-related fractures, such as femoral neck and vertebral fractures, are common in aged people, resulting in increased disability rate and health-care costs. Thus, it is of great importance to clarify the mechanism of osteoclast-related osteoporosis and find effective ways to avoid its complication. In this study, gene expression profile analysis and real-time polymerase chain reaction revealed that DUSP6 expression was suppressed in human and mice osteoporosis cases. In vitro experiments confirmed that DUSP6 overexpression prevented osteoclastogenesis, whereas inhibition of DUSP6 by small interference RNA or with a chemical inhibitor, (E/Z)-BCI, had the opposite effect. (E/Z)-BCl significantly accelerated the bone loss process in vivo by enhancing osteoclastogenesis. Bioinformatics analyses and in vitro experiments indicated that miR-181a was an upstream regulator of DUSP6. Moreover, miR-181a positively induced the differentiation and negatively regulated the apoptosis of osteoclasts via DUSP6. Furthermore, downstream signals by ERK2 and SMAD2 were also found to be involved in this process. Evaluation of ERK2-deficiency bone marrow-derived macrophages confirmed the role of ERK2 signaling in the DUSP6-mediated osteoclastogenesis. Additionally, immunoprecipitation assays confirmed that DUSP6 directly modified the phosphorylation status of SMAD2 and the subsequent nuclear transportation of NFATC1 to regulate osteoclast differentiation. Altogether, this study demonstrated for the first time the role of miRNA-181a/DUSP6 in the progression of osteoporosis via the ERK2 and SMAD2 signaling pathway. Hence, DUSP6 may represent a novel target for the treatment of osteoclast-related diseases in the future.

RevDate: 2021-09-02

Shen Y, Wang J, S Navlakha (2021)

A Correspondence between Normalization Strategies in Artificial and Biological Neural Networks.

Neural computation pii:107074 [Epub ahead of print].

A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods such as batch normalization, weight normalization, and their many variants help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this article, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neuron's activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent-that is, both push activation patterns of hidden units toward a homeostatic state, where all neurons are equally used-and we argue that such representations can improve coding capacity, discrimination, and regularization. As a proof of concept, we develop an algorithm, inspired by a neural normalization technique called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several data sets. Overall, we hope this bidirectional connection will inspire neuroscientists and machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.

RevDate: 2021-09-02

Xu R, Spataro R, Allison BZ, et al (2021)

Brain-Computer Interfaces in Acute and Subacute Disorders of Consciousness.

Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society pii:00004691-900000000-99262 [Epub ahead of print].

SUMMARY: Disorders of consciousness include coma, unresponsive wakefulness syndrome (also known as vegetative state), and minimally conscious state. Neurobehavioral scales such as coma recovery scale-revised are the gold standard for disorder of consciousness assessment. Brain-computer interfaces have been emerging as an alternative tool for these patients. The application of brain-computer interfaces in disorders of consciousness can be divided into four fields: assessment, communication, prediction, and rehabilitation. The operational theoretical model of consciousness that brain-computer interfaces explore was reviewed in this article, with a focus on studies with acute and subacute patients. We then proposed a clinically friendly guideline, which could contribute to the implementation of brain-computer interfaces in neurorehabilitation settings. Finally, we discussed limitations and future directions, including major challenges and possible solutions.

RevDate: 2021-09-26

Zhang H, Zhu L, Xu S, et al (2021)

Two brains, one target: Design of a multi-level information fusion model based on dual-subject RSVP.

Journal of neuroscience methods, 363:109346.

BACKGROUND: Rapid serial visual presentation (RSVP) based brain-computer interface (BCI) is widely used to categorize the target and non-target images. The available information limits the prediction accuracy of single-trial using single-subject electroencephalography (EEG) signals. New Method. Hyperscanning is a new manner to record two or more subjects' signals simultaneously. So we designed a multi-level information fusion model for target image detection based on dual-subject RSVP, namely HyperscanNet. The two modules of this model fuse the data and features of the two subjects at the data and feature layers. A chunked long and short-term memory artificial neural network (LSTM) was used in the time dimension to extract features at different periods separately, completing fine-grained underlying feature extraction. While the feature layer is fused, some plain operations are used to complete the fusion of the data layer to ensure that important information is not missed.

RESULTS: Experimental results show that the F1-score (the harmonic mean of precision and recall) of this method with best group of channels and segment length is 82.76%. Comparison with existing methods. This method improves the F1-score by at least 5% compared to single-subject target detection.

CONCLUSIONS: Target detection can be accomplished by the two subjects' collaboration to achieve a higher and more stable F1-score than a single subject.

RevDate: 2021-09-02

Waqar M, Mohamed S, Dulhanty L, et al (2021)

Radiologically defined acute hydrocephalus in aneurysmal subarachnoid haemorrhage.

British journal of neurosurgery [Epub ahead of print].

BACKGROUND: Ventriculomegaly is common in aneurysmal subarachnoid haemorrhage (aSAH). An imaging measure to predict the need for cerebrospinal fluid (CSF) diversion may be useful. The bicaudate index (BCI) has been previously applied to aSAH. Our aim was to derive and test a threshold BCI above which CSF diversion may be required.

METHODS: Review of prospective registry. The derivation group (2009-2015) included WFNS grade 1-2 aSAH patients who deteriorated clinically, had a repeat CT brain and underwent CSF diversion. BCI was measured on post-deterioration CTs and the lower limit of the 95% confidence interval (95%CI) was the hydrocephalus threshold. In a separate test group (2016), in WFNS ≥ 2 patients, we compared BCI on diagnostic CTs with CSF diversion within 24 hours.

RESULTS: The derivation group (n = 62) received an external ventricular (n = 57, 92%) or lumbar drain (n = 5, 8%). Mean post-deterioration BCI was 0.19 (95%CI 0.17-0.22) for age ≤49 years, 0.22 (95%CI 0.20-0.23) for age 50-64 years and 0.24 (95%CI 0.22-0.27) for age ≥65 years. Hydrocephalus thresholds were therefore 0.17, 0.20 and 0.22, respectively. In the test group (n = 105), there was no significant difference in BCI on the diagnostic CT between good and poor grade patients aged ≤49 years (p = 0.31) and ≥65 years (p = 0.96). 30/66 WFNS ≥ 2 patients underwent CSF diversion, although only 15/30 (50%) exceeded BCI thresholds for hydrocephalus.

CONCLUSION: A significant proportion of aSAH patients may undergo CSF diversion without objective evidence of hydrocephalus. Our threshold values require further testing but may provide an objective measure to aid clinical decision making in aSAH.

RevDate: 2021-09-03

Tarasenko A, Oganesyan M, Ivaskevych D, et al (2020)

Artificial Intelligence, Brains, and Beyond: Imperial College London Neurotechnology Symposium, 2020.

Bioelectricity, 2(3):310-313.

In this report, we give an overview of the proceedings from the online Imperial College London Neurotechnology Symposium 2020. The first part deals with the fundamentals of how artificial intelligence (AI) can be used to inform research frameworks used in the field of neurotechnology. The second part goes a level higher and shows how AI can be used in cutting-edge cellular and molecular methodologies and their applications. The final part focuses on the efforts to "decode" neural systems in brain-computer interfaces to advance neuroprosthetics.

RevDate: 2021-09-18

Jovanovic LI, Popovic MR, C Marquez-Chin (2021)

Characterizing the stimulation interference in electroencephalographic signals during brain-computer interface-controlled functional electrical stimulation therapy.

Artificial organs [Epub ahead of print].

INTRODUCTION: The integration of brain-computer interface (BCI) and functional electrical stimulation (FES) has brought about a new rehabilitation strategy: BCI-controlled FES therapy or BCI-FEST. During BCI-FEST, the stimulation is triggered by the patient's brain activity, often monitored using electroencephalography (EEG). Several studies have demonstrated that BCI-FEST can improve voluntary arm and hand function after an injury, but few studies have investigated the FES interference in EEG signals during BCI-FEST. In this study, we evaluated the effectiveness of band-pass filters, used to extract the BCI-relevant EEG components, in simultaneously reducing stimulation interference.

METHODS: We used EEG data from eight participants recorded during BCI-FEST. Additionally, we separately recorded the FES signal generated by the stimulator to estimate the spectral components of the FES interference, and extract the noise in time domain. Finally, we calculated signal-to-noise ratio (SNR) values before and after band-pass filtering, for two types of movements practiced during BCI-FEST: reaching and grasping.

RESULTS: The SNR values were greater after filtering across all participants for both movement types. For reaching movements, mean SNR values increased between 1.31 dB and 36.3 dB. Similarly, for grasping movements, mean SNR values increased between 2.82 dB and 40.16 dB, after filtering.

CONCLUSIONS: Band-pass filters, used to isolate EEG frequency bands for BCI application, were also effective in reducing stimulation interference. In addition, we provide a general algorithm that can be used in future studies to estimate the frequencies of FES interference as a function of the selected stimulation pulse frequency, FSTIM , and the EEG sampling rate, FS .

RevDate: 2021-08-31

Sharini H, Zolghadriha S, Riyahi Alam N, et al (2021)

Assessment of Motor Cortex in Active, Passive and Imagery Wrist Movement Using Functional MRI.

Journal of biomedical physics & engineering, 11(4):515-526.

Background: Functional Magnetic resonance imaging (fMRI) measures the small fluctuation of blood flow happening during task-fMRI in brain regions.

Objective: This research investigated these active, imagery and passive movements in volunteers design to permit a comparison of their capabilities in activating the brain areas.

Material and Methods: In this applied research, the activity of the motor cortex during the right-wrist movement was evaluated in 10 normal volunteers under active, passive, and imagery conditions. T2* weighted, three-dimensional functional images were acquired using a BOLD sensitive gradient-echo EPI (echo planar imaging) sequence with echo time (TE) of 30 ms and repetition time (TR) of 2000 ms. The functional data, which included 248 volumes per subject and condition, were acquired using the blocked design paradigm. The images were analyzed by the SPM12 toolbox, MATLAB software.

Results: The findings determined a significant increase in signal intensity of the motor cortex while performing the test compared to the rest time (p< 0.05). It was also observed that the active areas in hand representation of the motor cortex are different in terms of locations and the number of voxels in different wrist directions. Moreover, the findings showed that the position of active centers in the brain is different in active, passive, and imagery conditions.

Conclusion: Results confirm that primary motor cortex neurons play an essential role in the processing of complex information and are designed to control the direction of movement. It seems that the findings of this study can be applied for rehabilitation studies.

RevDate: 2021-08-31

Chakraborty S, Saetta G, Simon C, et al (2021)

Could Brain-Computer Interface Be a New Therapeutic Approach for Body Integrity Dysphoria?.

Frontiers in human neuroscience, 15:699830.

Patients suffering from body integrity dysphoria (BID) desire to become disabled, arising from a mismatch between the desired body and the physical body. We focus here on the most common variant, characterized by the desire for amputation of a healthy limb. In most reported cases, amputation of the rejected limb entirely alleviates the distress of the condition and engenders substantial improvement in quality of life. Since BID can lead to life-long suffering, it is essential to identify an effective form of treatment that causes the least amount of alteration to the person's anatomical structure and functionality. Treatment methods involving medications, psychotherapy, and vestibular stimulation have proven largely ineffective. In this hypothesis article, we briefly discuss the characteristics, etiology, and current treatment options available for BID before highlighting the need for new, theory driven approaches. Drawing on recent findings relating to functional and structural brain correlates of BID, we introduce the idea of brain-computer interface (BCI)/neurofeedback approaches to target altered patterns of brain activity, promote re-ownership of the limb, and/or attenuate stress and negativity associated with the altered body representation.

RevDate: 2021-09-26

Kline A, Forkert ND, Felfeliyan B, et al (2021)

fMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface.

Journal of neuroscience methods, 363:109339.

BACKGROUND: EEG and fMRI have contributed greatly to our understanding of brain activity and its link to behaviors by helping to identify both when and where the activity occurs. This is particularly important in the development of brain-computer interfaces (BCIs), where feed forward systems gather data from imagined brain activity and then send that information to an effector. The purpose of this study was to develop and evaluate a computational approach that enables an accurate mapping of spatial brain activity (fMRI) in relation to the temporal receptors (EEG electrodes) associated with imagined lower limb movement.

NEW METHOD: EEG and fMRI data from 16 healthy, male participants while imagining lower limb movement were used for this purpose. A combined analysis of fMRI data and EEG electrode locations was developed to identify EEG electrodes with a high likelihood of capturing imagined lower limb movement originating from various clusters of brain activity. This novel feature selection tool was used to develop an artificial neural network model to classify right and left lower limb movement.

RESULTS: Results showed that left versus right lower limb imagined movement could be classified with 66.5% accuracy using this approach. Comparison with existing methods: Adopting a purely data-driven approach for feature selection to use in the right/left classification task resulted in the same accuracy (66.6%) but with reduced interpretability.

CONCLUSIONS: The developed fMRI-informed EEG approach could pave the way towards improved brain computer interfaces for lower limb movement while also being applicable to other systems where fMRI could be helpful to inform EEG acquisition and processing.

RevDate: 2021-09-26

Verbaarschot C, Tump D, Lutu A, et al (2021)

A visual brain-computer interface as communication aid for patients with amyotrophic lateral sclerosis.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 132(10):2404-2415.

OBJECTIVE: Brain-Computer Interface (BCI) spellers that make use of code-modulated Visual Evoked Potentials (cVEP) may provide a fast and more accurate alternative to existing visual BCI spellers for patients with Amyotrophic Lateral Sclerosis (ALS). However, so far the cVEP speller has only been tested on healthy participants.

METHODS: We assess the brain responses, BCI performance and user experience of the cVEP speller in 20 healthy participants and 10 ALS patients. All participants performed a cued and free spelling task, and a free selection of Yes/No answers.

RESULTS: 27 out of 30 participants could perform the cued spelling task with an average accuracy of 79% for ALS patients, 88% for healthy older participants and 94% for healthy young participants. All 30 participants could answer Yes/No questions freely, with an average accuracy of around 90%.

CONCLUSIONS: With ALS patients typing on average 10 characters per minute, the cVEP speller presented in this paper outperforms other visual BCI spellers.

SIGNIFICANCE: These results support a general usability of cVEP signals for ALS patients, which may extend far beyond the tested speller to control e.g. an alarm, automatic door, or TV within a smart home.

RevDate: 2021-09-26

Daly I (2021)

Removal of physiological artifacts from simultaneous EEG and fMRI recordings.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 132(10):2371-2383.

OBJECTIVE: Simultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don't remove all other physiological artifacts.

METHODS: We propose a new offline EEG artifact removal method based upon a combination of independent component analysis and fMRI-based head movement estimation to aid the removal of physiological artifacts from EEG recorded during EEG-fMRI recordings. Our method makes novel use of head movement trajectories estimated from the fMRI recording in order to assist with identifying physiological artifacts in the EEG and is designed to be used after removal of the fMRI imaging artifact from the EEG.

RESULTS: We evaluate our method on EEG recorded during a joint EEG-fMRI session from healthy adult participants. Our method significantly reduces the influence of all types of physiological artifacts on the EEG. We also compare our method with a state-of-the-art physiological artifact removal method and demonstrate superior performance removing physiological artifacts.

CONCLUSIONS: Our proposed method is able to remove significantly more physiological artifact components from the EEG, recorded during a joint EEG-fMRI session, than other state-of-the-art methods.

SIGNIFICANCE: Our proposed method represents a marked improvement over current processing pipelines for removing physiological noise from EEG recorded during a joint EEG-fMRI session.

RevDate: 2021-09-02
CmpDate: 2021-08-31

Molina-Cantero AJ, Castro-García JA, Gómez-Bravo F, et al (2021)

Controlling a Mouse Pointer with a Single-Channel EEG Sensor.

Sensors (Basel, Switzerland), 21(16):.

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user's attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor's speed. The influence of the attention level on performance was studied. Additionally, Fitts' model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor's initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system's usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain-computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

RevDate: 2021-08-31
CmpDate: 2021-08-31

Won K, Kwon M, Ahn M, et al (2021)

Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI.

Sensors (Basel, Switzerland), 21(16):.

Brain-computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.

RevDate: 2021-08-31
CmpDate: 2021-08-31

Ikeda A, Y Washizawa (2021)

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks.

Sensors (Basel, Switzerland), 21(16):.

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain-computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

RevDate: 2021-09-16
CmpDate: 2021-08-31

De la Cruz-Guevara DR, Alfonso-Morales W, E Caicedo-Bravo (2021)

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach.

Sensors (Basel, Switzerland), 21(16):.

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate-ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches-the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey's test.

RevDate: 2021-08-31
CmpDate: 2021-08-31

Holdengreber E, Yozevitch R, V Khavkin (2021)

Intuitive Cognition-Based Method for Generating Speech Using Hand Gestures.

Sensors (Basel, Switzerland), 21(16):.

Muteness at its various levels is a common disability. Most of the technological solutions to the problem creates vocal speech through the transition from mute languages to vocal acoustic sounds. We present a new approach for creating speech: a technology that does not require prior knowledge of sign language. This technology is based on the most basic level of speech according to the phonetic division into vowels and consonants. The speech itself is expected to be expressed through sensing of the hand movements, as the movements are divided into three rotations: yaw, pitch, and roll. The proposed algorithm converts these rotations through programming to vowels and consonants. For the hand movement sensing, we used a depth camera and standard speakers in order to produce the sounds. The combination of the programmed depth camera and the speakers, together with the cognitive activity of the brain, is integrated into a unique speech interface. Using this interface, the user can develop speech through an intuitive cognitive process in accordance with the ongoing brain activity, similar to the natural use of the vocal cords. Based on the performance of the presented speech interface prototype, it is substantiated that the proposed device could be a solution for those suffering from speech disabilities.

RevDate: 2021-08-31
CmpDate: 2021-08-31

Zhang X, Hou W, Wu X, et al (2021)

Enhancing Detection of SSMVEP Induced by Action Observation Stimuli Based on Task-Related Component Analysis.

Sensors (Basel, Switzerland), 21(16):.

Action observation (AO)-based brain-computer interface (BCI) is an important technology in stroke rehabilitation training. It has the advantage of simultaneously inducing steady-state motion visual evoked potential (SSMVEP) and activating sensorimotor rhythm. Moreover, SSMVEP could be utilized to perform classification. However, SSMVEP is composed of complex modulation frequencies. Traditional canonical correlation analysis (CCA) suffers from poor recognition performance in identifying those modulation frequencies at short stimulus duration. To address this issue, task-related component analysis (TRCA) was utilized to deal with SSMVEP for the first time. An interesting phenomenon was found: different modulated frequencies in SSMVEP distributed in different task-related components. On this basis, a multi-component TRCA method was proposed. All the significant task-related components were utilized to construct multiple spatial filters to enhance the detection of SSMVEP. Further, a combination of TRCA and CCA was proposed to utilize both advantages. Results showed that the accuracies using the proposed methods were significant higher than that using CCA at all window lengths and significantly higher than that using ensemble-TRCA at short window lengths (≤2 s). Therefore, the proposed methods further validate the induced modulation frequencies and will speed up the application of the AO-based BCI in rehabilitation.

RevDate: 2021-08-28

Yoshimura N, Umetsu K, Tonin A, et al (2021)

Binary Semantic Classification Using Cortical Activation with Pavlovian-Conditioned Vestibular Responses in Healthy and Locked-In Individuals.

Cerebral cortex communications, 2(3):tgab046.

To develop a more reliable brain-computer interface (BCI) for patients in the completely locked-in state (CLIS), here we propose a Pavlovian conditioning paradigm using galvanic vestibular stimulation (GVS), which can induce a strong sensation of equilibrium distortion in individuals. We hypothesized that associating two different sensations caused by two-directional GVS with the thoughts of "yes" and "no" by individuals would enable us to emphasize the differences in brain activity associated with the thoughts of yes and no and hence help us better distinguish the two from electroencephalography (EEG). We tested this hypothesis with 11 healthy and 1 CLIS participant. Our results showed that, first, conditioning of GVS with the thoughts of yes and no is possible. And second, the classification of whether an individual is thinking "yes" or "no" is significantly improved after the conditioning, even in the absence of subsequent GVS stimulations. We observed average classification accuracy of 73.0% over 11 healthy individuals and 85.3% with the CLIS patient. These results suggest the establishment of GVS-based Pavlovian conditioning and its usability as a noninvasive BCI.

RevDate: 2021-08-27

Nason SR, Mender MJ, Letner JG, et al (2021)

Restoring upper extremity function with brain-machine interfaces.

International review of neurobiology, 159:153-186.

One of the most exciting advances to emerge in neural interface technologies has been the development of real-time brain-machine interface (BMI) neuroprosthetic devices to restore upper extremity function. BMI neuroprostheses, made possible by synergistic advances in neural recording technologies, high-speed computation and signal processing, and neuroscience, have permitted the restoration of volitional movement to patients suffering the loss of upper-extremity function. In this chapter, we review the scientific and technological advances underlying these remarkable devices. After presenting an introduction to the current state of the field, we provide an accessible technical discussion of the two fundamental requirements of a successful neuroprosthesis: signal extraction from the brain and signal decoding that results in robust prosthetic control. We close with a presentation of emerging technologies that are likely to substantially advance the field.

RevDate: 2021-09-15
CmpDate: 2021-09-15

Abend A, Steele C, Jahnke HG, et al (2021)

Adhesion of Neurons and Glial Cells with Nanocolumnar TiN Films for Brain-Machine Interfaces.

International journal of molecular sciences, 22(16):.

Coupling of cells to biomaterials is a prerequisite for most biomedical applications; e.g., neuroelectrodes can only stimulate brain tissue in vivo if the electric signal is transferred to neurons attached to the electrodes' surface. Besides, cell survival in vitro also depends on the interaction of cells with the underlying substrate materials; in vitro assays such as multielectrode arrays determine cellular behavior by electrical coupling to the adherent cells. In our study, we investigated the interaction of neurons and glial cells with different electrode materials such as TiN and nanocolumnar TiN surfaces in contrast to gold and ITO substrates. Employing single-cell force spectroscopy, we quantified short-term interaction forces between neuron-like cells (SH-SY5Y cells) and glial cells (U-87 MG cells) for the different materials and contact times. Additionally, results were compared to the spreading dynamics of cells for different culture times as a function of the underlying substrate. The adhesion behavior of glial cells was almost independent of the biomaterial and the maximum growth areas were already seen after one day; however, adhesion dynamics of neurons relied on culture material and time. Neurons spread much better on TiN and nanocolumnar TiN and also formed more neurites after three days in culture. Our designed nanocolumnar TiN offers the possibility for building miniaturized microelectrode arrays for impedance spectroscopy without losing detection sensitivity due to a lowered self-impedance of the electrode. Hence, our results show that this biomaterial promotes adhesion and spreading of neurons and glial cells, which are important for many biomedical applications in vitro and in vivo.

RevDate: 2021-08-31
CmpDate: 2021-08-31

Sánchez-Cuesta FJ, Arroyo-Ferrer A, González-Zamorano Y, et al (2021)

Clinical Effects of Immersive Multimodal BCI-VR Training after Bilateral Neuromodulation with rTMS on Upper Limb Motor Recovery after Stroke. A Study Protocol for a Randomized Controlled Trial.

Medicina (Kaunas, Lithuania), 57(8):.

Background and Objectives: The motor sequelae after a stroke are frequently persistent and cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the cortico-spinal pathways are known to cause a reduction of cortical excitability in the lesioned area not only for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action. Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation (rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS have been shown to increase the excitability of the lesioned hemisphere. Mental representation techniques, neurofeedback, and virtual reality have also been shown to increase cortical excitability and complement conventional rehabilitation. Materials and Methods: We aim to carry out a single-blind, randomized, controlled trial aiming to study the efficacy of immersive multimodal Brain-Computer Interfacing-Virtual Reality (BCI-VR) training after bilateral neuromodulation with rTMS on upper limb motor recovery after subacute stroke (>3 months) compared to neuromodulation combined with conventional motor imagery tasks. This study will include 42 subjects in a randomized controlled trial design. The main expected outcomes are changes in the Motricity Index of the Arm (MI), dynamometry of the upper limb, score according to Fugl-Meyer for upper limb (FMA-UE), and changes in the Stroke Impact Scale (SIS). The evaluation will be carried out before the intervention, after each intervention and 15 days after the last session. Conclusions: This trial will show the additive value of VR immersive motor imagery as an adjuvant therapy combined with a known effective neuromodulation approach opening new perspectives for clinical rehabilitation protocols.

RevDate: 2021-08-29

Almarri B, Rajasekaran S, CH Huang (2021)

Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.

PloS one, 16(8):e0253383.

The dimensionality of the spatially distributed channels and the temporal resolution of electroencephalogram (EEG) based brain-computer interfaces (BCI) undermine emotion recognition models. Thus, prior to modeling such data, as the final stage of the learning pipeline, adequate preprocessing, transforming, and extracting temporal (i.e., time-series signals) and spatial (i.e., electrode channels) features are essential phases to recognize underlying human emotions. Conventionally, inter-subject variations are dealt with by avoiding the sources of variation (e.g., outliers) or turning the problem into a subject-deponent. We address this issue by preserving and learning from individual particularities in response to affective stimuli. This paper investigates and proposes a subject-independent emotion recognition framework that mitigates the subject-to-subject variability in such systems. Using an unsupervised feature selection algorithm, we reduce the feature space that is extracted from time-series signals. For the spatial features, we propose a subject-specific unsupervised learning algorithm that learns from inter-channel co-activation online. We tested this framework on real EEG benchmarks, namely DEAP, MAHNOB-HCI, and DREAMER. We train and test the selection outcomes using nested cross-validation and a support vector machine (SVM). We compared our results with the state-of-the-art subject-independent algorithms. Our results show an enhanced performance by accurately classifying human affection (i.e., based on valence and arousal) by 16%-27% compared to other studies. This work not only outperforms other subject-independent studies reported in the literature but also proposes an online analysis solution to affection recognition.

RevDate: 2021-08-31

Benatti HR, Luz HR, Lima DM, et al (2021)

Morphometric Patterns and Blood Biochemistry of Capybaras (Hydrochoerus hydrochaeris) from Human-Modified Landscapes and Natural Landscapes in Brazil.

Veterinary sciences, 8(8):.

The capybara, Hydrochoerus hydrochaeris, is the largest extant rodent of the world. To better understand the correlation between size and body mass, and biochemical parameters of capybaras from areas with different degrees of anthropization (i.e., different food supplies), we sampled free-ranging capybaras from areas of natural landscapes (NLs) and human-modified landscapes (HMLs) in Brazil. Analyses of biometrical and biochemical parameters of capybaras showed that animals from HMLs were heavier (higher body mass) than those from NL, a condition possibly related to fat deposit rather than body length, as indicated by Body Condition Index (BCI) analyses. Biochemical parameters indicated higher serum levels of albumin, creatine kinase, cholesterol, fructosamine and total protein among capybaras from HMLs than from NLs; however, when all adult capybaras were analyzed together only cholesterol and triglycerides were positively correlated with body mass. We propose that the biochemical profile differences between HMLs and NLs are related to the obesity condition of capybaras among HMLs. Considering that heavier animals might live longer and reproduce more often, our results could have important implications in the population dynamics of capybaras among HMLs, where this rodent species is frequently represented by overgrowth populations that generate several levels of conflicts with human beings.

RevDate: 2021-09-17
CmpDate: 2021-09-17

Servick K (2021)

Brain signals 'speak' for person with paralysis.

Science (New York, N.Y.), 373(6552):263.

RevDate: 2021-08-29

Mian SY, Honey JR, Carnicer-Lombarte A, et al (2021)

Large Animal Studies to Reduce the Foreign Body Reaction in Brain-Computer Interfaces: A Systematic Review.

Biosensors, 11(8):.

Brain-computer interfaces (BCI) are reliant on the interface between electrodes and neurons to function. The foreign body reaction (FBR) that occurs in response to electrodes in the brain alters this interface and may pollute detected signals, ultimately impeding BCI function. The size of the FBR is influenced by several key factors explored in this review; namely, (a) the size of the animal tested, (b) anatomical location of the BCI, (c) the electrode morphology and coating, (d) the mechanics of electrode insertion, and (e) pharmacological modification (e.g., drug eluting electrodes). Trialing methods to reduce FBR in vivo, particularly in large models, is important to enable further translation in humans, and we systematically reviewed the literature to this effect. The OVID, MEDLINE, EMBASE, SCOPUS and Scholar databases were searched. Compiled results were analysed qualitatively. Out of 8388 yielded articles, 13 were included for analysis, with most excluded studies experimenting on murine models. Cats, rabbits, and a variety of breeds of minipig/marmoset were trialed. On average, over 30% reduction in inflammatory cells of FBR on post mortem histology was noted across intervention groups. Similar strategies to those used in rodent models, including tip modification and flexible and sinusoidal electrode configurations, all produced good effects in histology; however, a notable absence of trials examining the effect on BCI end-function was noted. Future studies should assess whether the reduction in FBR correlates to an improvement in the functional effect of the intended BCI.

RevDate: 2021-08-27

Song X, Zeng Y, Tong L, et al (2021)

P3-MSDA: Multi-Source Domain Adaptation Network for Dynamic Visual Target Detection.

Frontiers in human neuroscience, 15:685173.

Single-trial electroencephalogram detection has been widely applied in brain-computer interface (BCI) systems. Moreover, an individual generalized model is significant for applying the dynamic visual target detection BCI system in real life because of the time jitter of the detection latency, the dynamics and complexity of visual background. Hence, we developed an unsupervised multi-source domain adaptation network (P3-MSDA) for dynamic visual target detection. In this network, a P3 map-clustering method was proposed for source domain selection. The adversarial domain adaptation was conducted for domain alignment to eliminate individual differences, and prediction probabilities were ranked and returned to guide the input of target samples for imbalanced data classification. The results showed that individuals with a strong P3 map selected by the proposed P3 map-clustering method perform best on the source domain. Compared with existing schemes, the proposed P3-MSDA network achieved the highest classification accuracy and F1 score using five labeled individuals with a strong P3 map as the source domain. These findings can have a significant meaning in building an individual generalized model for dynamic visual target detection.

RevDate: 2021-08-27

Fekri Azgomi H, Hahn JO, RT Faghih (2021)

Closed-Loop Fuzzy Energy Regulation in Patients With Hypercortisolism via Inhibitory and Excitatory Intermittent Actuation.

Frontiers in neuroscience, 15:695975.

Hypercortisolism or Cushing's disease, which corresponds to the excessive levels of cortisol hormone, is associated with tiredness and fatigue during the day and disturbed sleep at night. Our goal is to employ a wearable brain machine interface architecture to regulate one's energy levels in hypercortisolism. In the present simulation study, we generate multi-day cortisol profile data for ten subjects both in healthy and disease conditions. To relate an internal hidden cognitive energy state to one's cortisol secretion patterns, we employ a state-space model. Particularly, we consider circadian upper and lower bound envelopes on cortisol levels, and timings of hypothalamic pulsatile activity underlying cortisol secretions as continuous and binary observations, respectively. To estimate the hidden cognitive energy-related state, we use Bayesian filtering. In our proposed architecture, we infer one's cognitive energy-related state using wearable devices rather than monitoring the brain activity directly and close the loop utilizing fuzzy control. To model actuation in the real-time closed-loop architecture, we simulate two types of medications that result in increasing and decreasing the energy levels in the body. Finally, we close the loop using a knowledge-based control approach. The results on ten simulated profiles verify how the proposed architecture is able to track the energy state and regulate it using hypothetical medications. In a simulation study based on experimental data, we illustrate the feasibility of designing a wearable brain machine interface architecture for energy regulation in hypercortisolism. This simulation study is a first step toward the ultimate goal of managing hypercortisolism in real-world situations.

RevDate: 2021-09-24

Bruurmijn LCM, Raemaekers M, Branco MP, et al (2021)

Decoding attempted phantom hand movements from ipsilateral sensorimotor areas after amputation.

Journal of neural engineering, 18(5):.

Objective.The sensorimotor cortex is often selected as target in the development of a Brain-Computer Interface, as activation patterns from this region can be robustly decoded to discriminate between different movements the user executes. Up until recently, such BCIs were primarily based on activity in the contralateral hemisphere, where decoding movements still works even years after denervation. However, there is increasing evidence for a role of the sensorimotor cortex in controlling the ipsilateral body. The aim of this study is to investigate the effects of denervation on the movement representation on the ipsilateral sensorimotor cortex.Approach.Eight subjects with acquired above-elbow arm amputation and nine controls performed a task in which they made (or attempted to make with their phantom hand) six different gestures from the American Manual Alphabet. Brain activity was measured using 7T functional MRI, and a classifier was trained to discriminate between activation patterns on four different regions of interest (ROIs) on the ipsilateral sensorimotor cortex.Main results.Classification scores showed that decoding was possible and significantly better than chance level for both the phantom and intact hands from all ROIs. Decoding both the left (intact) and right (phantom) hand from the same hemisphere was also possible with above-chance level classification score.Significance.The possibility to decode both hands from the same hemisphere, even years after denervation, indicates that implantation of motor-electrodes for BCI control possibly need only cover a single hemisphere, making surgery less invasive, and increasing options for people with lateralized damage to motor cortex like after stroke.

RevDate: 2021-09-07

Tabernig CB, Carrere LC, Manresa JB, et al (2021)

Does feedback based on FES-evoked nociceptive withdrawal reflex condition event-related desynchronization? An exploratory study with brain-computer interfaces.

Biomedical physics & engineering express, 7(6):.

Introduction.Event-related desynchronization (ERD) is used in brain-computer interfaces (BCI) to detect the user's motor intention (MI) and convert it into a command for an actuator to provide sensory feedback or mobility, for example by means of functional electrical stimulation (FES). Recent studies have proposed to evoke the nociceptive withdrawal reflex (NWR) using FES, in order to evoke synergistic movements of the lower limb and to facilitate the gait rehabilitation of stroke patients. The use of NWR to provide sensorimotor feedback in ERD-based BCI is novel; thererfore, the conditioning effect that nociceptive stimuli might have on MI is still unknown.Objetive.To assess the ERD produced during the MI after FES-evoked NWR, in order to evaluate if nociceptive stimuli condition subsequent ERDs.Methods. Data from 528 electroencephalography trials of 8 healthy volunteers were recorded and analyzed. Volunteers used an ERD-based BCI, which provided two types of feedback: intrisic by the FES-evoked NWR and extrinsic by virtual reality. The electromyogram of the tibialis anterior muscle was also recorded. The main outcome variables were the normalized root mean square of the evoked electromyogram (RMSnorm), the average electroencephalogram amplitude at the ERD frequency during MI (A¯MI) and the percentage decrease ofA¯MIrelative to rest (ERD%) at the first MI subsequent to the activation of the BCI.Results.No evidence of changes of theRMSnormon both theA¯MI(p = 0.663) and theERD%(p = 0.252) of the subsequent MI was detected. A main effect of the type of feedback was found in the subsequentA¯MI(p < 0.001), with intrinsic feedback resulting in a largerA¯MI.Conclusions.No evidence of ERD conditioning was observed using BCI feedback based on FES-evoked NWR .Significance.FES-evoked NWR could constitute a potential feedback modality in an ERD-based BCI to facilitate motor recovery of stroke people.

RevDate: 2021-09-25

Neumann WJ, Memarian Sorkhabi M, Benjaber M, et al (2021)

The sensitivity of ECG contamination to surgical implantation site in brain computer interfaces.

Brain stimulation, 14(5):1301-1306.

BACKGROUND: Brain sensing devices are approved today for Parkinson's, essential tremor, and epilepsy therapies. Clinical decisions for implants are often influenced by the premise that patients will benefit from using sensing technology. However, artifacts, such as ECG contamination, can render such treatments unreliable. Therefore, clinicians need to understand how surgical decisions may affect artifact probability.

OBJECTIVES: Investigate neural signal contamination with ECG activity in sensing enabled neurostimulation systems, and in particular clinical choices such as implant location that impact signal fidelity.

METHODS: Electric field modeling and empirical signals from 85 patients were used to investigate the relationship between implant location and ECG contamination.

RESULTS: The impact on neural recordings depends on the difference between ECG signal and noise floor of the electrophysiological recording. Empirically, we demonstrate that severe ECG contamination was more than 3.2x higher in left-sided subclavicular implants (48.3%), when compared to right-sided implants (15.3%). Cranial implants did not show ECG contamination.

CONCLUSIONS: Given the relative frequency of corrupted neural signals, we conclude that implant location will impact the ability of brain sensing devices to be used for "closed-loop" algorithms. Clinical adjustments such as implant location can significantly affect signal integrity and need consideration.

RevDate: 2021-08-27

Bodenham RF, Mazeri S, Cleaveland S, et al (2021)

Latent class evaluation of the performance of serological tests for exposure to Brucella spp. in cattle, sheep, and goats in Tanzania.

PLoS neglected tropical diseases, 15(8):e0009630.

BACKGROUND: Brucellosis is a neglected zoonosis endemic in many countries, including regions of sub-Saharan Africa. Evaluated diagnostic tools for the detection of exposure to Brucella spp. are important for disease surveillance and guiding prevention and control activities.

METHODS AND FINDINGS: Bayesian latent class analysis was used to evaluate performance of the Rose Bengal plate test (RBT) and a competitive ELISA (cELISA) in detecting Brucella spp. exposure at the individual animal-level for cattle, sheep, and goats in Tanzania. Median posterior estimates of RBT sensitivity were: 0.779 (95% Bayesian credibility interval (BCI): 0.570-0.894), 0.893 (0.636-0.989), and 0.807 (0.575-0.966), and for cELISA were: 0.623 (0.443-0.790), 0.409 (0.241-0.644), and 0.561 (0.376-0.713), for cattle, sheep, and goats, respectively. Sensitivity BCIs were wide, with the widest for cELISA in sheep. RBT and cELISA median posterior estimates of specificity were high across species models: RBT ranged between 0.989 (0.980-0.998) and 0.995 (0.985-0.999), and cELISA between 0.984 (0.974-0.995) and 0.996 (0.988-1). Each species model generated seroprevalence estimates for two livestock subpopulations, pastoralist and non-pastoralist. Pastoralist seroprevalence estimates were: 0.063 (0.045-0.090), 0.033 (0.018-0.049), and 0.051 (0.034-0.076), for cattle, sheep, and goats, respectively. Non-pastoralist seroprevalence estimates were below 0.01 for all species models. Series and parallel diagnostic approaches were evaluated. Parallel outperformed a series approach. Median posterior estimates for parallel testing were ≥0.920 (0.760-0.986) for sensitivity and ≥0.973 (0.955-0.992) for specificity, for all species models.

CONCLUSIONS: Our findings indicate that Brucella spp. surveillance in Tanzania using RBT and cELISA in parallel at the animal-level would give high test performance. There is a need to evaluate strategies for implementing parallel testing at the herd- and flock-level. Our findings can assist in generating robust Brucella spp. exposure estimates for livestock in Tanzania and wider sub-Saharan Africa. The adoption of locally evaluated robust diagnostic tests in setting-specific surveillance is an important step towards brucellosis prevention and control.

RevDate: 2021-09-07

Xu B, Wang Y, Deng L, et al (2021)

Decoding Hand Movement Types and Kinematic Information From Electroencephalogram.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 29:1744-1755.

Brain-computer interfaces (BCIs) have achieved successful control of assistive devices, e.g. neuroprosthesis or robotic arm. Previous research based on hand movements Electroencephalogram (EEG) has shown limited success in precise and natural control. In this study, we explored the possibilities of decoding movement types and kinematic information for three reach-and-execute actions using movement-related cortical potentials (MRCPs). EEG signals were acquired from 12 healthy subjects during the execution of pinch, palmar and precision disk rotation actions that involved two levels of speeds and forces. In the case of discrimination between hand movement types under each of four different kinematics conditions, we obtained the average peak accuracies of 83.44% and 73.83% for the binary and 3-class classification, respectively. In the case of discrimination between different movement kinematics for each of three actions, the average peak accuracies of 82.9% and 58.2% could be achieved for the two and 4-class scenario. In both cases, peak decoding performance was significantly higher than the subject-specific chance level. We found that hand movement types all could be classified when these actions were executed at four different kinematic parameters. Meanwhile, for each of three hand movements, we decoded movement parameters successfully. Furthermore, the feasibility of decoding hand movements during hand retraction process was also demonstrated. These findings are of great importance for controlling neuroprosthesis or other rehabilitation devices in a fine and natural way, which would drastically increase the acceptance of motor impaired users.

RevDate: 2021-09-07

Habibzadeh H, Norton JJS, Vaughan TM, et al (2021)

A Voting-Enhanced Dynamic-Window-Length Classifier for SSVEP-Based BCIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 29:1766-1773.

We present a dynamic window-length classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that does not require the user to choose a feature extraction method or channel set. Instead, the classifier uses multiple feature extraction methods and channel selections to infer the SSVEP and relies on majority voting to pick the most likely target. The classifier extends the window length dynamically if no target obtains the majority of votes. Compared with existing solutions, our classifier: (i) does not assume that any single feature extraction method will consistently outperform the others; (ii) adapts the channel selection to individual users or tasks; (iii) uses dynamic window lengths; (iv) is unsupervised (i.e., does not need training). Collectively, these characteristics make the classifier easy-to-use, especially for caregivers and others with limited technical expertise. We evaluated the performance of our classifier on a publicly available benchmark dataset from 35 healthy participants. We compared the information transfer rate (ITR) of this new classifier to those of the minimum energy combination (MEC), maximum synchronization index (MSI), and filter bank canonical correlation analysis (FBCCA). The new classifier increases average ITR to 123.5 bits-per-minute (bpm), 47.5, 51.2, and 19.5 bpm greater than the MEC, MSI, and FBCCA classifiers, respectively.

RevDate: 2021-08-31

Ali A, Liaqat S, Tariq H, et al (2021)

Neonatal calf diarrhea: A potent reservoir of multi-drug resistant bacteria, environmental contamination and public health hazard in Pakistan.

The Science of the total environment, 799:149450 pii:S0048-9697(21)04524-1 [Epub ahead of print].

Though emergence of multi-drug resistant bacteria in the environment is a demonstrated worldwide phenomenon, limited research is reported about the prevalence of resistant bacteria in fecal ecology of neonatal calf diarrhea (NCD) animals in Pakistan. The present study aimed to identify and assess the prevalence of bacterial pathogens and their resistance potential in the fecal ecology of NCD diseased animals of Pakistan. The presence of antibiotic resistance genes (blaTEM, blaNDM-1, blaCTX-M, qnrS) was also investigated. A total of 51 bacterial isolates were recovered from feces of young diarrheic animals (n = 11), collected from 7 cities of Pakistan and identified on the basis of 16S rRNA gene sequence and phylogenetic analysis. Selected isolates were subjected to antimicrobial susceptibility by disc diffusion method while polymerase chain reaction (PCR) was used to characterize the blaTEM, blaNDM-1, blaCTX-M, qnrS and mcr-1 antibiotic resistance genes. Based on the 16S rRNA gene sequences (Accession numbers: LC488898 to LC488948), all isolates were identified that belonged to seventeen genera with the highest prevalence rate for phylum Proteobacteria and genus Bacillus (23%). Antibiotic susceptibility explained the prevalence of resistance in isolates ciprofloxacin (100%), ampicillin (100%), sulfamethoxazole-trimethoprim (85%), tetracycline (75%), amoxicillin (55%), ofloxacin (50%), ceftazidime (45%), amoxicillin/clavulanic acid (45%), levofloxacin (30%), cefpodoxime (25%), cefotaxime (25%), cefotaxime/clavulanic acid (20%), and imipenem (10%). MICs demonstrated that almost 90% isolates were multi-drug resistant (against at least three antibiotics), specially against ciprofloxacin, and tetracycline with the highest resistance levels for Shigella sp. (NCCP-421) (MIC-CIP up to 75 μg mL-1) and Escherichia sp. (NCCP-432) (MIC-TET up to 250 μg mL-1). PCR-assisted detection of antibiotic resistance genes showed that 54% isolates were positive for blaTEM gene, 7% isolates were positive for blaCTX-M gene, 23% isolates were positive for each of qnrS and mcr-1 genes, 23% isolates were co-positive in combinations of qnrS and mcr-1 genes and blaTEM and mcr-1 genes, whereas none of the isolate showed presence of blaNDM-1 gene.

RevDate: 2021-09-24

Larzabal C, Bonnet S, Costecalde T, et al (2021)

Long-term stability of the chronic epidural wireless recorder WIMAGINE in tetraplegic patients.

Journal of neural engineering, 18(5):.

Objective.The evaluation of the long-term stability of ElectroCorticoGram (ECoG) signals is an important scientific question as new implantable recording devices can be used for medical purposes such as Brain-Computer Interface (BCI) or brain monitoring.Approach.The long-term clinical validation of wireless implantable multi-channel acquisition system for generic interface with neurons (WIMAGINE), a wireless 64-channel epidural ECoG recorder was investigated. The WIMAGINE device was implanted in two quadriplegic patients within the context of a BCI protocol. This study focused on the ECoG signal stability in two patients bilaterally implanted in June 2017 (P1) and in November 2019 (P2).Methods. The ECoG signal was recorded at rest prior to each BCI session resulting in a 32 month and in a 14 month follow-up for P1 and P2 respectively. State-of-the-art signal evaluation metrics such as root mean square (RMS), the band power (BP), the signal to noise ratio (SNR), the effective bandwidth (EBW) and the spectral edge frequency (SEF) were used to evaluate stability of signal over the implantation time course. The time-frequency maps obtained from task-related motor activations were also studied to investigate the long-term selectivity of the electrodes.Mainresults.Based on temporal linear regressions, we report a limited decrease of the signal average level (RMS), spectral distribution (BP) and SNR, and a remarkable steadiness of the EBW and SEF. Time-frequency maps obtained during motor imagery, showed a high level of discrimination 1 month after surgery and also after 2 years.Conclusions.The WIMAGINE epidural device showed high stability over time. The signal evaluation metrics of two quadriplegic patients during 32 months and 14 months respectively provide strong evidence that this wireless implant is well-suited for long-term ECoG recording.Significance.These findings are relevant for the future of implantable BCIs, and could benefit other patients with spinal cord injury, amyotrophic lateral sclerosis, neuromuscular diseases or drug-resistant epilepsy.

RevDate: 2021-08-31

Sun J, He J, X Gao (2021)

Neurofeedback Training of the Control Network Improves Children's Performance with an SSVEP-based BCI.

Neuroscience pii:S0306-4522(21)00416-4 [Epub ahead of print].

In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and in neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, there has been little research aiming to improve the performance of brain-computer interfaces (BCIs) using neuromodulation. The present study presents a novel design for a neurofeedback training (NFT) method to improve the operation of a steady-state visual evoked potential (SSVEP)-based BCI and further explores its underlying mechanisms. The use of NFT to upregulate alpha-band power in the user's parietal lobe is presented in this study as a new neuromodulation method to improve SSVEP-based BCI in this study. After users completed this NFT intervention, the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of the SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6%, respectively. However, no improvement was observed in the control group in which the subjects did not participate in NFT. Moreover, a general reinforcement of the information flow from the parietal lobe to the occipital lobe was observed. Evidence from a network analysis and an attention test further indicates that NFT improves attention by developing the control capacity of the parietal lobe and then enhances the above SSVEP indicators. Upregulating the amplitude of parietal alpha oscillations using NFT significantly improves the SSVEP-based BCI performance by modulating the control network. The study validates an effective neuromodulation method and possibly contributes to explaining the function of the parietal lobe in the control network.

RevDate: 2021-09-20
CmpDate: 2021-09-20

Li MA, Han JF, JF Yang (2021)

Automatic feature extraction and fusion recognition of motor imagery EEG using multilevel multiscale CNN.

Medical & biological engineering & computing, 59(10):2037-2050.

A motor imagery EEG (MI-EEG) signal is often selected as the driving signal in an active brain computer interface (BCI) system, and it has been a popular field to recognize MI-EEG images via convolutional neural network (CNN), which poses a potential problem for maintaining the integrity of the time-frequency-space information in MI-EEG images and exploring the feature fusion mechanism in the CNN. However, information is excessively compressed in the present MI-EEG image, and the sequential CNN is unfavorable for the comprehensive utilization of local features. In this paper, a multidimensional MI-EEG imaging method is proposed, which is based on time-frequency analysis and the Clough-Tocher (CT) interpolation algorithm. The time-frequency matrix of each electrode is generated via continuous wavelet transform (WT), and the relevant section of frequency is extracted and divided into nine submatrices, the longitudinal sums and lengths of which are calculated along the directions of frequency and time successively to produce a 3 × 3 feature matrix for each electrode. Then, feature matrix of each electrode is interpolated to coincide with their corresponding coordinates, thereby yielding a WT-based multidimensional image, called WTMI. Meanwhile, a multilevel and multiscale feature fusion convolutional neural network (MLMSFFCNN) is designed for WTMI, which has dense information, low signal-to-noise ratio, and strong spatial distribution. Extensive experiments are conducted on the BCI Competition IV 2a and 2b datasets, and accuracies of 92.95% and 97.03% are yielded based on 10-fold cross-validation, respectively, which exceed those of the state-of-the-art imaging methods. The kappa values and p values demonstrate that our method has lower class skew and error costs. The experimental results demonstrate that WTMI can fully represent the time-frequency-space features of MI-EEG and that MLMSFFCNN is beneficial for improving the collection of multiscale features and the fusion recognition of general and abstract features for WTMI.

RevDate: 2021-08-24

Trinh TT, Tsai CF, Hsiao YT, et al (2021)

Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs.

Frontiers in computational neuroscience, 15:700467.

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

RevDate: 2021-08-24

Di Liberto GM, Marion G, SA Shamma (2021)

Accurate Decoding of Imagined and Heard Melodies.

Frontiers in neuroscience, 15:673401.

Music perception requires the human brain to process a variety of acoustic and music-related properties. Recent research used encoding models to tease apart and study the various cortical contributors to music perception. To do so, such approaches study temporal response functions that summarise the neural activity over several minutes of data. Here we tested the possibility of assessing the neural processing of individual musical units (bars) with electroencephalography (EEG). We devised a decoding methodology based on a maximum correlation metric across EEG segments (maxCorr) and used it to decode melodies from EEG based on an experiment where professional musicians listened and imagined four Bach melodies multiple times. We demonstrate here that accurate decoding of melodies in single-subjects and at the level of individual musical units is possible, both from EEG signals recorded during listening and imagination. Furthermore, we find that greater decoding accuracies are measured for the maxCorr method than for an envelope reconstruction approach based on backward temporal response functions (bTRF env). These results indicate that low-frequency neural signals encode information beyond note timing, especially with respect to low-frequency cortical signals below 1 Hz, which are shown to encode pitch-related information. Along with the theoretical implications of these results, we discuss the potential applications of this decoding methodology in the context of novel brain-computer interface solutions.

RevDate: 2021-09-03

Bhattacharyya S, Valeriani D, Cinel C, et al (2021)

Anytime collaborative brain-computer interfaces for enhancing perceptual group decision-making.

Scientific reports, 11(1):17008.

In this paper we present, and test in two realistic environments, collaborative Brain-Computer Interfaces (cBCIs) that can significantly increase both the speed and the accuracy of perceptual group decision-making. The key distinguishing features of this work are: (1) our cBCIs combine behavioural, physiological and neural data in such a way as to be able to provide a group decision at any time after the quickest team member casts their vote, but the quality of a cBCI-assisted decision improves monotonically the longer the group decision can wait; (2) we apply our cBCIs to two realistic scenarios of military relevance (patrolling a dark corridor and manning an outpost at night where users need to identify any unidentified characters that appear) in which decisions are based on information conveyed through video feeds; and (3) our cBCIs exploit Event-Related Potentials (ERPs) elicited in brain activity by the appearance of potential threats but, uniquely, the appearance time is estimated automatically by the system (rather than being unrealistically provided to it). As a result of these elements, in the two test environments, groups assisted by our cBCIs make both more accurate and faster decisions than when individual decisions are integrated in more traditional manners.

RevDate: 2021-08-20

Chen H, Lu F, Guo X, et al (2021)

Dimensional Analysis of Atypical Functional Connectivity of Major Depression Disorder and Bipolar Disorder.

Cerebral cortex (New York, N.Y. : 1991) pii:6355470 [Epub ahead of print].

Literatures have reported considerable heterogeneity with atypical functional connectivity (FC) pattern of psychiatric disorders. However, traditional statistical methods are hard to explore this heterogeneity pattern. We proposed a "brain dimension" method to describe the atypical FC patterns of major depressive disorder and bipolar disorder (BD). The approach was firstly applied to a simulation dataset. It was then utilized to a real resting-state functional magnetic resonance imaging dataset of 47 individuals with major depressive disorder, 32 individuals with BD, and 52 well matched health controls. Our method showed a better ability to extract the FC dimensions than traditional methods. The results of the real dataset revealed atypical FC dimensions for major depressive disorder and BD. Especially, an atypical FC dimension which exhibited decreased FC strength of thalamus and basal ganglia was found with higher severity level of individuals with BD than the ones with major depressive disorder. This study provided a novel "brain dimension" method to view the atypical FC patterns of major depressive disorder and BD and revealed shared and specific atypical FC patterns between major depressive disorder and BD.


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.

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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

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Collection of publications by R J Robbins

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