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30 Mar 2023 at 01:38
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Bibliography on: Brain-Computer Interface


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RJR: Recommended Bibliography 30 Mar 2023 at 01:38 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 OR brain-machine OR mind-machine OR neural-control interface) NOT 26799652[PMID] ) NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)


RevDate: 2023-03-29

Li J, Cheng Y, Gu M, et al (2023)

Sensing and Stimulation Applications of Carbon Nanomaterials in Implantable Brain-Computer Interface.

International journal of molecular sciences, 24(6): pii:ijms24065182.

Implantable brain-computer interfaces (BCIs) are crucial tools for translating basic neuroscience concepts into clinical disease diagnosis and therapy. Among the various components of the technological chain that increases the sensing and stimulation functions of implanted BCI, the interface materials play a critical role. Carbon nanomaterials, with their superior electrical, structural, chemical, and biological capabilities, have become increasingly popular in this field. They have contributed significantly to advancing BCIs by improving the sensor signal quality of electrical and chemical signals, enhancing the impedance and stability of stimulating electrodes, and precisely modulating neural function or inhibiting inflammatory responses through drug release. This comprehensive review provides an overview of carbon nanomaterials' contributions to the field of BCI and discusses their potential applications. The topic is broadened to include the use of such materials in the field of bioelectronic interfaces, as well as the potential challenges that may arise in future implantable BCI research and development. By exploring these issues, this review aims to provide insight into the exciting developments and opportunities that lie ahead in this rapidly evolving field.

RevDate: 2023-03-29

Sheng J, Xu J, Li H, et al (2023)

A Multi-Scale Temporal Convolutional Network with Attention Mechanism for Force Level Classification during Motor Imagery of Unilateral Upper-Limb Movements.

Entropy (Basel, Switzerland), 25(3): pii:e25030464.

In motor imagery (MI) brain-computer interface (BCI) research, some researchers have designed MI paradigms of force under a unilateral upper-limb static state. It is difficult to apply these paradigms to the dynamic force interaction process between the robot and the patient in a brain-controlled rehabilitation robot system, which needs to induce thinking states of the patient's demand for assistance. Therefore, in our research, according to the movement of wiping the table in human daily life, we designed a three-level-force MI paradigm under a unilateral upper-limb dynamic state. Based on the event-related de-synchronization (ERD) feature analysis of the electroencephalography (EEG) signals generated by the brain's force change motor imagination, we proposed a multi-scale temporal convolutional network with attention mechanism (MSTCN-AM) algorithm to recognize ERD features of MI-EEG signals. Aiming at the slight feature differences of single-trial MI-EEG signals among different levels of force, the MSTCN module was designed to extract fine-grained features of different dimensions in the time-frequency domain. The spatial convolution module was then used to learn the area differences of space domain features. Finally, the attention mechanism dynamically weighted the time-frequency-space domain features to improve the algorithm's sensitivity. The results showed that the accuracy of the algorithm was 86.4 ± 14.0% for the three-level-force MI-EEG data collected experimentally. Compared with the baseline algorithms (OVR-CSP+SVM (77.6 ± 14.5%), Deep ConvNet (75.3 ± 12.3%), Shallow ConvNet (77.6 ± 11.8%), EEGNet (82.3 ± 13.8%), and SCNN-BiLSTM (69.1 ± 16.8%)), our algorithm had higher classification accuracy with significant differences and better fitting performance.

RevDate: 2023-03-29

García-Murillo DG, Álvarez-Meza AM, CG Castellanos-Dominguez (2023)

KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification.

Diagnostics (Basel, Switzerland), 13(6): pii:diagnostics13061122.

This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject's unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain-computer interface systems.

RevDate: 2023-03-29

Liu Y, Zhang Y, Jiang Z, et al (2023)

Exploring Neural Mechanisms of Reward Processing Using Coupled Matrix Tensor Factorization: A Simultaneous EEG-fMRI Investigation.

Brain sciences, 13(3): pii:brainsci13030485.

BACKGROUND: It is crucial to understand the neural feedback mechanisms and the cognitive decision-making of the brain during the processing of rewards. Here, we report the first attempt for a simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) study in a gambling task by utilizing tensor decomposition.

METHODS: First, the single-subject EEG data are represented as a third-order spectrogram tensor to extract frequency features. Next, the EEG and fMRI data are jointly decomposed into a superposition of multiple sources characterized by space-time-frequency profiles using coupled matrix tensor factorization (CMTF). Finally, graph-structured clustering is used to select the most appropriate model according to four quantitative indices.

RESULTS: The results clearly show that not only are the regions of interest (ROIs) found in other literature activated, but also the olfactory cortex and fusiform gyrus which are usually ignored. It is found that regions including the orbitofrontal cortex and insula are activated for both winning and losing stimuli. Meanwhile, regions such as the superior orbital frontal gyrus and anterior cingulate cortex are activated upon winning stimuli, whereas the inferior frontal gyrus, cingulate cortex, and medial superior frontal gyrus are activated upon losing stimuli.

CONCLUSION: This work sheds light on the reward-processing progress, provides a deeper understanding of brain function, and opens a new avenue in the investigation of neurovascular coupling via CMTF.

RevDate: 2023-03-29

Xu D, Tang F, Li Y, et al (2023)

An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.

Brain sciences, 13(3): pii:brainsci13030483.

The brain-computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011-2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals.

RevDate: 2023-03-29

Wang T, Chen YH, M Sawan (2023)

Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study.

Bioengineering (Basel, Switzerland), 10(3): pii:bioengineering10030281.

Motor imagery-based brain-computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.

RevDate: 2023-03-27

Zhang T, Rahimi Azghadi M, Lammie C, et al (2023)

Spike sorting algorithms and their efficient hardware implementation: A comprehensive survey.

Journal of neural engineering [Epub ahead of print].

Spike sorting is a set of techniques used to analyze extracellular neural recordings, attributing individual spikes to individual neurons. This field has gained significant interest in neuroscience due to advances in implantable microelectrode arrays, capable of recording thousands of neurons simultaneously. High-density electrodes, combined with efficient and accurate spike sorting systems, are essential for various applications, including Brain Machine Interfaces (BMI), experimental neural prosthetics, real-time neurological disorder monitoring, and neuroscience research. However, given the resource constraints of modern applications, relying solely on algorithmic innovation is not enough. Instead, a co-optimization approach that combines hardware and spike sorting algorithms must be taken to develop neural recording systems suitable for resource-constrained environments, such as wearable devices and BMIs. This co-design requires careful consideration when selecting appropriate spike-sorting algorithms that match specific hardware and use cases. Approach: We investigated the recent literature on spike sorting, both in terms of hardware advancements and algorithms innovations. Moreover, we dedicated special attention to identifying suitable algorithm-hardware combinations, and their respective real-world applicabilities. Main Results: In this review, we first examined the current progress in algorithms, and described the recent departure from the conventional "3- step" algorithms in favor of more advanced template matching or machine-learning-based techniques. Next, we explored innovative hardware options, including Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), and In-Memory Computing Devices (IMCs). Additionally, the challenges and future opportunities for spike sorting are discussed. Significance: This comprehensive review systematically summarizes the latest spike sorting techniques and demonstrates how they enable researchers to overcome traditional obstacles and unlock novel applications. Our goal is for this work to serve as a roadmap for future researchers seeking to identify the most appropriate spike sorting implementations for various experimental settings. By doing so, we aim to facilitate the advancement of this exciting field and promote the development of innovative solutions that drive progress in neural engineering research.

RevDate: 2023-03-28

Rueckauer B, M van Gerven (2023)

An in-silico framework for modeling optimal control of neural systems.

Frontiers in neuroscience, 17:1141884.

INTRODUCTION: Brain-machine interfaces have reached an unprecedented capacity to measure and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor function. Classical control theory is pushed to its limit when aiming to design control laws that are suitable for large-scale, complex neural systems. This work proposes a scalable, data-driven, unified approach to study brain-machine-environment interaction using established tools from dynamical systems, optimal control theory, and deep learning.

METHODS: To unify the methodology, we define the environment, neural system, and prosthesis in terms of differential equations with learnable parameters, which effectively reduce to recurrent neural networks in the discrete-time case. Drawing on tools from optimal control, we describe three ways to train the system: Direct optimization of an objective function, oracle-based learning, and reinforcement learning. These approaches are adapted to different assumptions about knowledge of system equations, linearity, differentiability, and observability.

RESULTS: We apply the proposed framework to train an in-silico neural system to perform tasks in a linear and a nonlinear environment, namely particle stabilization and pole balancing. After training, this model is perturbed to simulate impairment of sensor and motor function. We show how a prosthetic controller can be trained to restore the behavior of the neural system under increasing levels of perturbation.

DISCUSSION: We expect that the proposed framework will enable rapid and flexible synthesis of control algorithms for neural prostheses that reduce the need for in-vivo testing. We further highlight implications for sparse placement of prosthetic sensor and actuator components.

RevDate: 2023-03-28

Hu YT, Chen XL, Zhang YN, et al (2023)

Sex differences in hippocampal β-amyloid accumulation in the triple-transgenic mouse model of Alzheimer's disease and the potential role of local estrogens.

Frontiers in neuroscience, 17:1117584.

INTRODUCTION: Epidemiological studies show that women have a higher prevalence of Alzheimer's disease (AD) than men. Peripheral estrogen reduction during aging in women is proposed to play a key role in this sex-associated prevalence, however, the underlying mechanism remains elusive. We previously found that transcription factor early growth response-1 (EGR1) significantly regulates cholinergic function. EGR1 stimulates acetylcholinesterase (AChE) gene expression and is involved in AD pathogenesis. We aimed to investigate whether the triple-transgenic AD (3xTg-AD) mice harboring PS1 [M146V] , APP [Swe] , and Tau [P301L] show sex differences in β-amyloid (Aβ) and hyperphosphorylated tau (p-Tau), the two primary AD hallmarks, and how local 17β-estradiol (E2) may regulate the expression of EGR1 and AChE.

METHODS: We first sacrificed male and female 3xTg-AD mice at 3-4, 7-8, and 11-12 months and measured the levels of Aβ, p-Tau, EGR1, and AChE in the hippocampal complex. Second, we infected SH-SY5Y cells with lentivirus containing the amyloid precursor protein construct C99, cultured with or without E2 administration we measured the levels of extracellular Aβ and intracellular EGR1 and AChE.

RESULTS: Female 3xTg-AD mice had higher levels of Aβ compared to males, while no p-Tau was found in either group. In SH-SY5Y cells infected with lentivirus containing the amyloid precursor protein construct C99, we observed significantly increased extracellular Aβ and decreased expression of intracellular EGR1 and AChE. By adding E2 to the culture medium, extracellular Aβ(l-42) was significantly decreased while intracellular EGR1 and AChE expression were elevated.

DISCUSSION: This data shows that the 3xTg-AD mouse model can be useful for studying the human sex differences of AD, but only in regards to Ap. Furthermore, in vitro data shows local E2 may be protective for EGR1 and cholinergic functions in AD while suppressing soluble Aβ(1-42) levels. Altogether, this study provides further in vivo and in vitro data supporting the human epidemiological data indicating a higher prevalence of AD in women is related to changes in brain estrogen levels.

RevDate: 2023-03-28
CmpDate: 2023-03-28

Carpenter KA, Thurlow KE, Craig SEL, et al (2023)

Wnt regulation of hematopoietic stem cell development and disease.

Current topics in developmental biology, 153:255-279.

Hematopoietic stem cells (HSCs) are multipotent stem cells that give rise to all cells of the blood and most immune cells. Due to their capacity for unlimited self-renewal, long-term HSCs replenish the blood and immune cells of an organism throughout its life. HSC development, maintenance, and differentiation are all tightly regulated by cell signaling pathways, including the Wnt pathway. Wnt signaling is initiated extracellularly by secreted ligands which bind to cell surface receptors and give rise to several different downstream signaling cascades. These are classically categorized either β-catenin dependent (BCD) or β-catenin independent (BCI) signaling, depending on their reliance on the β-catenin transcriptional activator. HSC development, homeostasis, and differentiation is influenced by both BCD and BCI, with a high degree of sensitivity to the timing and dosage of Wnt signaling. Importantly, dysregulated Wnt signals can result in hematological malignancies such as leukemia, lymphoma, and myeloma. Here, we review how Wnt signaling impacts HSCs during development and in disease.

RevDate: 2023-03-24

Wu H, Xie Q, Pan J, et al (2023)

Identifying Patients with Cognitive Motor Dissociation Using Resting-state Temporal Stability.

NeuroImage pii:S1053-8119(23)00196-9 [Epub ahead of print].

Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.

RevDate: 2023-03-24

Chen XL, Fortes JM, Hu YT, et al (2023)

Sexually dimorphic age-related molecular differences in the entorhinal cortex of cognitively intact elderly: Relation to early Alzheimer's changes.

Alzheimer's & dementia : the journal of the Alzheimer's Association [Epub ahead of print].

INTRODUCTION: Women are more vulnerable to Alzheimer's disease (AD) than men. The entorhinal cortex (EC) is one of the earliest structures affected in AD. We identified in cognitively intact elderly different molecular changes in the EC in relation to age.

METHODS: Changes in 12 characteristic molecules in relation to age were determined by quantitative immunohistochemistry or in situ hybridization in the EC. They were arbitrarily grouped into sex steroid-related molecules, markers of neuronal activity, neurotransmitter-related molecules, and cholinergic activity-related molecules.

RESULTS: The changes in molecules indicated increasing local estrogenic and neuronal activity accompanied by a higher and faster hyperphosphorylated tau accumulation in women's EC in relation to age, versus a mainly stable local estrogenic/androgenic and neuronal activity in men's EC.

DISCUSSION: EC employs a different neurobiological strategy in women and men to maintain cognitive function, which seems to be accompanied by an earlier start of AD in women.

HIGHLIGHTS: Local estrogen system is activated with age only in women's entorhinal cortex (EC). EC neuronal activity increased with age only in elderly women with intact cognition. Men and women have different molecular strategies to retain cognition with aging. P-tau accumulation in the EC was higher and faster in cognitively intact elderly women.

RevDate: 2023-03-25

Ma Z, Wang K, Xu M, et al (2023)

Transformed common spatial pattern for motor imagery-based brain-computer interfaces.

Frontiers in neuroscience, 17:1116721.

OBJECTIVE: The motor imagery (MI)-based brain-computer interface (BCI) is one of the most popular BCI paradigms. Common spatial pattern (CSP) is an effective algorithm for decoding MI-related electroencephalogram (EEG) patterns. However, it highly depends on the selection of EEG frequency bands. To address this problem, previous researchers often used a filter bank to decompose EEG signals into multiple frequency bands before applying the traditional CSP.

APPROACH: This study proposed a novel method, i.e., transformed common spatial pattern (tCSP), to extract the discriminant EEG features from multiple frequency bands after but not before CSP. To verify its effectiveness, we tested tCSP on a dataset collected by our team and a public dataset from BCI competition III. We also performed an online evaluation of the proposed method.

MAIN RESULTS: As a result, for the dataset collected by our team, the classification accuracy of tCSP was significantly higher than CSP by about 8% and filter bank CSP (FBCSP) by about 4.5%. The combination of tCSP and CSP further improved the system performance with an average accuracy of 84.77% and a peak accuracy of 100%. For dataset IVa in BCI competition III, the combination method got an average accuracy of 94.55%, which performed best among all the presented CSP-based methods. In the online evaluation, tCSP and the combination method achieved an average accuracy of 80.00 and 84.00%, respectively.

SIGNIFICANCE: The results demonstrate that the frequency band selection after CSP is better than before for MI-based BCIs. This study provides a promising approach for decoding MI EEG patterns, which is significant for the development of BCIs.

RevDate: 2023-03-26

Velasco I, Sipols A, De Blas CS, et al (2023)

Motor imagery EEG signal classification with a multivariate time series approach.

Biomedical engineering online, 22(1):29.

BACKGROUND: Electroencephalogram (EEG) signals record electrical activity on the scalp. Measured signals, especially EEG motor imagery signals, are often inconsistent or distorted, which compromises their classification accuracy. Achieving a reliable classification of motor imagery EEG signals opens the door to possibilities such as the assessment of consciousness, brain computer interfaces or diagnostic tools. We seek a method that works with a reduced number of variables, in order to avoid overfitting and to improve interpretability. This work aims to enhance EEG signal classification accuracy by using methods based on time series analysis. Previous work on this line, usually took a univariate approach, thus losing the possibility to take advantage of the correlation information existing within the time series provided by the different electrodes. To overcome this problem, we propose a multivariate approach that can fully capture the relationships among the different time series included in the EEG data. To perform the multivariate time series analysis, we use a multi-resolution analysis approach based on the discrete wavelet transform, together with a stepwise discriminant that selects the most discriminant variables provided by the discrete wavelet transform analysis RESULTS: Applying this methodology to EEG data to differentiate between the motor imagery tasks of moving either hands or feet has yielded very good classification results, achieving in some cases up to 100% of accuracy for this 2-class pre-processed dataset. Besides, the fact that these results were achieved using a reduced number of variables (55 out of 22,176) can shed light on the relevance and impact of those variables.

CONCLUSIONS: This work has a potentially large impact, as it enables classification of EEG data based on multivariate time series analysis in an interpretable way with high accuracy. The method allows a model with a reduced number of features, facilitating its interpretability and improving overfitting. Future work will extend the application of this classification method to help in diagnosis procedures for detecting brain pathologies and for its use in brain computer interfaces. In addition, the results presented here suggest that this method could be applied to other fields for the successful analysis of multivariate temporal data.

RevDate: 2023-03-23

Saraswat M, AK Dubey (2023)

EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface.

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

Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in brain-computer interface (BCI) systems. Due to massive and myriads data, the signals are appealed in a non-stationary format that ends with a poor quality resolution. To overcome this existing issue, a new framework of enhanced deep learning methods is proposed. The source signals are collected and undergo feature extraction in four ways. Hence, the features are concatenated to enhance the performance. Subsequently, the concatenated features are given to probability ratio-based Reptile Search Algorithm (PR-RSA) to select the optimal features. Finally, the classification is conducted using Enhanced Bi-directional Long Short-Term Memory (EBi-LSTM), where the hyperparameters are optimized by PR-RSA. Throughout the result analysis, it is confirmed that the offered model obtains elevated classification accuracy, and thus tends to increase the performance.

RevDate: 2023-03-24

Fang H, Y Yang (2023)

Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study.

Frontiers in computational neuroscience, 17:1119685.

INTRODUCTION: Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.

METHODS: Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.

RESULTS: We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.

DISCUSSION: Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.

RevDate: 2023-03-24

Moly A, Aksenov A, Martel F, et al (2023)

Online adaptive group-wise sparse Penalized Recursive Exponentially Weighted N-way Partial Least Square for epidural intracranial BCI.

Frontiers in human neuroscience, 17:1075666.

INTRODUCTION: Motor Brain-Computer Interfaces (BCIs) create new communication pathways between the brain and external effectors for patients with severe motor impairments. Control of complex effectors such as robotic arms or exoskeletons is generally based on the real-time decoding of high-resolution neural signals. However, high-dimensional and noisy brain signals pose challenges, such as limitations in the generalization ability of the decoding model and increased computational demands.

METHODS: The use of sparse decoders may offer a way to address these challenges. A sparsity-promoting penalization is a common approach to obtaining a sparse solution. BCI features are naturally structured and grouped according to spatial (electrodes), frequency, and temporal dimensions. Applying group-wise sparsity, where the coefficients of a group are set to zero simultaneously, has the potential to decrease computational time and memory usage, as well as simplify data transfer. Additionally, online closed-loop decoder adaptation (CLDA) is known to be an efficient procedure for BCI decoder training, taking into account neuronal feedback. In this study, we propose a new algorithm for online closed-loop training of group-wise sparse multilinear decoders using L p -Penalized Recursive Exponentially Weighted N-way Partial Least Square (PREW-NPLS). Three types of sparsity-promoting penalization were explored using L p with p = 0., 0.5, and 1.

RESULTS: The algorithms were tested offline in a pseudo-online manner for features grouped by spatial dimension. A comparison study was conducted using an epidural ECoG dataset recorded from a tetraplegic individual during long-term BCI experiments for controlling a virtual avatar (left/right-hand 3D translation). Novel algorithms showed comparable or better decoding performance than conventional REW-NPLS, which was achieved with sparse models. The proposed algorithms are compatible with real-time CLDA.

DISCUSSION: The proposed algorithm demonstrated good performance while drastically reducing the computational load and the memory consumption. However, the current study is limited to offline computation on data recorded with a single patient, with penalization restricted to the spatial domain only.

RevDate: 2023-03-22

Gao X, Zhang S, Liu K, et al (2023)

An Adaptive Joint CCA-ICA Method for Ocular Artifact Removal and its Application to Emotion Classification.

Journal of neuroscience methods pii:S0165-0270(23)00060-2 [Epub ahead of print].

BACKGROUND: The quality of Electroencephalogram (EEG) signals is critical for revealing the neural mechanism of emotions. However, ocular artifacts decreased the signal to noise ratio (SNR) and covered the inherent cognitive component of EEGs, which pose a great challenge in neuroscience research.

NEW METHOD: We proposed a novel unsupervised learning algorithm to adaptively remove the ocular artifacts by combining canonical correlation analysis (CCA), independent component analysis (ICA), higher-order statistics, empirical mode decomposition (EMD), and wavelet denoising techniques. Specifically, the combination of CCA and ICA aimed to improve the quality of source separation, while the higher-order statistics further located the source of ocular artifacts. Subsequently, these noised sources were further corrected by EMD and wavelet denoising to improve SNR of EEG signals.

RESULTS: We evaluated the performance of our proposed method with simulation studies and real EEG applications. The results of simulation study showed our proposed method could significantly improve the quality of signals under almost all noise conditions compared to four state-of-art methods. Consistently, the experiments of real EEG applications showed that the proposed methods could efficiently restrict the components of ocular artifacts and preserve the inherent information of cognition processing to improve the reliability of related analysis such as power spectral density (PSD) and emotion recognition.

Our proposed model outperforms the comparative methods in EEG recovery, which further improve the application performance such as PSD analysis and emotion recognition.

CONCLUSIONS: The superior performance of our proposed method suggests that it is promising for removing ocular artifacts from EEG signals, which offers an efficient EEG preprocessing technology for the development of brain computer interface such as emotion recognition.

RevDate: 2023-03-22

Lebani BR, Barcelos ADS, Gouveia DSES, et al (2023)

The role of transurethral resection of prostate (TURP) in patients with underactive bladder: 12 months follow-up in different grades of detrusor contractility.

The Prostate [Epub ahead of print].

INTRODUCTION AND OBJECTIVE: Male detrusor underactivity (DUA) definition remains controversial and no effective treatment is consolidated. Transurethral resection of the prostate (TURP) is one of the cornerstones surgical treatments recommended in bladder outlet obstruction (BOO). However, the role of prostatic surgery in male DUA is not clear. The primary endpoint was the clinical and voiding improvement based on IPSS and the maximum flow rate in uroflowmetry (Qmax) within 12 months.

MATERIALS AND METHODS: We analyzed an ongoing prospective database that embraces benign prostata hyperplasia (BPH) male patients with lower urinary tract symptoms who have undergone to TURP. All patients were evaluated pre and postoperatively based on IPSS questionnaires, prostate volume measured by ultrasound, postvoid residual urine volume (PVR), Prostate Specific Antigen measurement and urodynamic study (UDS) before the procedure. After surgery, all patients were evaluated at 1-, 3-, 6- and 12-months. Patients were categorized in 3 groups: Group 1-Detrusor Underactive (Bladder Contractility Index (BCI) [BCI] < 100 and BOO index [BOOI] < 40); Group 2-Detrusor Underactive and BOO (BCI < 100 and BOOI ≥ 40); Group 3-BOO (BCI ≥ 100 and BOOI ≥ 0).

RESULTS: It was included 158 patients underwent monopolar or bipolar TURP since November 2015 to March 2021. According to UDS, patients were categorized in: group 1 (n = 39 patients); group 2 (n = 41 patients); group 3 (n = 77 patients). Preoperative IPSS was similar between groups (group 1-24.9 ± 6.33; group 2-24.8 ± 7.33; group 3-24.5 ± 6.23). Qmax was statistically lower in the group 2 (group 1-5.43 ± 3.69; group 2-3.91 ± 2.08; group 3-6.3 ± 3.18) as well as greater PVR. The 3 groups presented similar outcomes regard to IPSS score during the follow-up. There was a significant increase in Qmax in the 3 groups. However, group 1 presented the lowest Qmax improvement.

CONCLUSION: There were different objective outcomes depending on the degree of DUA at 12 months follow-up. Patients with DUA had similar IPSS improvement. However, DUA patients had worst Qmax improvement than men with normal bladder contraction.

RevDate: 2023-03-20

Karoly HC, Drennan ML, Prince MA, et al (2023)

Consuming oral cannabidiol prior to a standard alcohol dose has minimal effect on breath alcohol level and subjective effects of alcohol.

Psychopharmacology [Epub ahead of print].

RATIONALE: Cannabidiol (CBD) is found in the cannabis plant and has garnered attention as a potential treatment for alcohol use disorder (AUD). CBD reduces alcohol consumption and other markers of alcohol dependence in rodents, but human research on CBD and alcohol is limited. It is unknown whether CBD reduces drinking in humans, and mechanisms through which CBD could impact behavioral AUD phenotypes are unknown.

OBJECTIVES: This study explores effects of oral CBD on breath alcohol level (BrAC), and subjective effects of alcohol in human participants who report heavy drinking.

METHODS: In this placebo-controlled, crossover study, participants consumed 30 mg CBD, 200 mg CBD, or placebo CBD before receiving a standardized alcohol dose. Participants were blind to which CBD dose they received at each session and completed sessions in random order. Thirty-six individuals completed at least one session and were included in analyses.

RESULTS: Differences in outcomes across the three conditions and by sex were explored using multilevel structural equation models. BrAC fell fastest in the placebo condition, followed by 30 mg and 200 mg CBD. Stimulation decreased more slowly in the 200 mg CBD condition than in placebo (b =  - 2.38, BCI [- 4.46, - .03]). Sedation decreased more slowly in the 30 mg CBD condition than in placebo (b =  - 2.41, BCI [- 4.61, - .09]). However, the magnitude of condition differences in BrAC and subjective effects was trivial.

CONCLUSIONS: CBD has minimal influence on BrAC and subjective effects of alcohol. Further research is needed to test whether CBD impacts alcohol consumption in humans, and if so, what mechanism(s) may explain this effect.

RevDate: 2023-03-21

Hanna J, A Flöel (2023)

An accessible and versatile deep learning-based sleep stage classifier.

Frontiers in neuroinformatics, 17:1086634.

Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.

RevDate: 2023-03-21

Sawai S, Murata S, Fujikawa S, et al (2023)

Effects of neurofeedback training combined with transcranial direct current stimulation on motor imagery: A randomized controlled trial.

Frontiers in neuroscience, 17:1148336.

INTRODUCTION: Neurofeedback (NFB) training and transcranial direct current stimulation (tDCS) have been shown to individually improve motor imagery (MI) abilities. However, the effect of combining both of them with MI has not been verified. Therefore, the aim of this study was to examine the effect of applying tDCS directly before MI with NFB.

METHODS: Participants were divided into an NFB group (n = 10) that performed MI with NFB and an NFB + tDCS group (n = 10) that received tDCS for 10 min before MI with NFB. Both groups performed 60 MI trials with NFB. The MI task was performed 20 times without NFB before and after training, and μ-event-related desynchronization (ERD) and vividness MI were evaluated.

RESULTS: μ-ERD increased significantly in the NFB + tDCS group compared to the NFB group. MI vividness significantly increased before and after training.

DISCUSSION: Transcranial direct current stimulation and NFB modulate different processes with respect to MI ability improvement; hence, their combination might further improve MI performance. The results of this study indicate that the combination of NFB and tDCS for MI is more effective in improving MI abilities than applying them individually.

RevDate: 2023-03-21

Lai D, Wan Z, Lin J, et al (2023)

Neuronal representation of bimanual arm motor imagery in the motor cortex of a tetraplegia human, a pilot study.

Frontiers in neuroscience, 17:1133928.

INTRODUCTION: How the human brain coordinates bimanual movements is not well-established.

METHODS: Here, we recorded neural signals from a paralyzed individual's left motor cortex during both unimanual and bimanual motor imagery tasks and quantified the representational interaction between arms by analyzing the tuning parameters of each neuron.

RESULTS: We found a similar proportion of neurons preferring each arm during unimanual movements, however, when switching to bimanual movements, the proportion of contralateral preference increased to 71.8%, indicating contralateral lateralization. We also observed a decorrelation process for each arm's representation across the unimanual and bimanual tasks. We further confined that these changes in bilateral relationships are mainly caused by the alteration of tuning parameters, such as the increased bilateral preferred direction (PD) shifts and the significant suppression in bilateral modulation depths (MDs), especially the ipsilateral side.

DISCUSSION: These results contribute to the knowledge of bimanual coordination and thus the design of cutting-edge bimanual brain-computer interfaces.

RevDate: 2023-03-20

Islam MK, A Rastegarnia (2023)

Editorial: Recent advances in EEG (non-invasive) based BCI applications.

Frontiers in computational neuroscience, 17:1151852.

RevDate: 2023-03-19

Gerasimov JY, Tu D, Hitaishi V, et al (2023)

A Biologically Interfaced Evolvable Organic Pattern Classifier.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Epub ahead of print].

Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process these data into meaningful information, and translate that information into a format that can be interpreted by living systems. Here, the first example of interfacing a hardware-based pattern classifier with a biological nerve is reported. The classifier implements the Widrow-Hoff learning algorithm on an array of evolvable organic electrochemical transistors (EOECTs). The EOECTs' channel conductance is modulated in situ by electropolymerizing the semiconductor material within the channel, allowing for low voltage operation, high reproducibility, and an improvement in state retention by two orders of magnitude over state-of-the-art OECT devices. The organic classifier is interfaced with a biological nerve using an organic electrochemical spiking neuron to translate the classifier's output to a simulated action potential. The latter is then used to stimulate muscle contraction selectively based on the input pattern, thus paving the way for the development of adaptive neural interfaces for closed-loop therapeutic systems.

RevDate: 2023-03-18

Wang A, Fan Z, Zhang Y, et al (2023)

Resting-state SEEG-Based Brain Network Analysis for the Detection of Epileptic Area.

Journal of neuroscience methods pii:S0165-0270(23)00058-4 [Epub ahead of print].

BACKGROUND: Most epilepsy research is based on interictal or ictal functional connectivity. However, prolonged electrode implantation may affect patients' health and the accuracy of epileptic zone identification. Brief resting-state SEEG recordings reduce the observation of epileptic discharges by reducing electrode implantation and other seizure-inducing interventions.

NEW METHOD: The location coordinates of SEEG in the brain were identified using CT and MRI. Based on undirected brain network connectivity, five functional connectivity measures and data feature vector centrality were calculated. Network connectivity was calculated from multiple perspectives of linear correlation, information theory, phase, and frequency, and the relative influence of nodes on network connectivity was considered. We investigated the potential value of resting-state SEEG for epileptic zone identification by comparing the differences between epileptic and non-epileptic zones, as well as the differences between patients with different surgical outcomes.

RESULTS: By comparing the centrality of brain network connectivity between epileptic and non-epileptic zones, we found significant differences in the distribution of brain networks between the two zones. There was a significant difference in brain network between patients with good surgical outcomes and those with poor surgical outcomes (p<0.01). By combining support vector machines with static node importance, we predicted an AUC of 0.94 ± 0.08 for the epilepsy zone.

CONCLUSIONS AND SIGNIFICANCE: The results illustrated that nodes in epileptic zones are distinct from those in non-epileptic zones. Analysis of resting-state SEEG data and the importance of nodes in the brain network may contribute to identifying the epileptic zone and predicting the outcome.

RevDate: 2023-03-20

Johnson KA, Worbe Y, Foote KD, et al (2023)

Neurosurgical lesioning for Tourette syndrome - Authors' reply.

The Lancet. Neurology, 22(4):292-293.

RevDate: 2023-03-20

Wang F, Chen Y, Lin Y, et al (2023)

A parabrachial to hypothalamic pathway mediates defensive behavior.

eLife, 12:.

Defensive behaviors are critical for animal's survival. Both the paraventricular nucleus of the hypothalamus (PVN) and the parabrachial nucleus (PBN) have been shown to be involved in defensive behaviors. However, whether there are direct connections between them to mediate defensive behaviors remains unclear. Here, by retrograde and anterograde tracing, we uncover that cholecystokinin (CCK)-expressing neurons in the lateral PBN (LPB[CCK]) directly project to the PVN. By in vivo fiber photometry recording, we find that LPB[CCK] neurons actively respond to various threat stimuli. Selective photoactivation of LPB[CCK] neurons promotes aversion and defensive behaviors. Conversely, photoinhibition of LPB[CCK] neurons attenuates rat or looming stimuli-induced flight responses. Optogenetic activation of LPB[CCK] axon terminals within the PVN or PVN glutamatergic neurons promotes defensive behaviors. Whereas chemogenetic and pharmacological inhibition of local PVN neurons prevent LPB[CCK]-PVN pathway activation-driven flight responses. These data suggest that LPB[CCK] neurons recruit downstream PVN neurons to actively engage in flight responses. Our study identifies a previously unrecognized role for the LPB[CCK]-PVN pathway in controlling defensive behaviors.

RevDate: 2023-03-17

Berger CC, Coppi S, HH Ehrsson (2023)

Synchronous motor imagery and visual feedback of finger movement elicit the moving rubber hand illusion, at least in illusion-susceptible individuals.

Experimental brain research [Epub ahead of print].

Recent evidence suggests that imagined auditory and visual sensory stimuli can be integrated with real sensory information from a different sensory modality to change the perception of external events via cross-modal multisensory integration mechanisms. Here, we explored whether imagined voluntary movements can integrate visual and proprioceptive cues to change how we perceive our own limbs in space. Participants viewed a robotic hand wearing a glove repetitively moving its right index finger up and down at a frequency of 1 Hz, while they imagined executing the corresponding movements synchronously or asynchronously (kinesthetic-motor imagery); electromyography (EMG) from the participants' right index flexor muscle confirmed that the participants kept their hand relaxed while imagining the movements. The questionnaire results revealed that the synchronously imagined movements elicited illusory ownership and a sense of agency over the moving robotic hand-the moving rubber hand illusion-compared with asynchronously imagined movements; individuals who affirmed experiencing the illusion with real synchronous movement also did so with synchronous imagined movements. The results from a proprioceptive drift task further demonstrated a shift in the perceived location of the participants' real hand toward the robotic hand in the synchronous versus the asynchronous motor imagery condition. These results suggest that kinesthetic motor imagery can be used to replace veridical congruent somatosensory feedback from a moving finger in the moving rubber hand illusion to trigger illusory body ownership and agency, but only if the temporal congruence rule of the illusion is obeyed. This observation extends previous studies on the integration of mental imagery and sensory perception to the case of multisensory bodily awareness, which has potentially important implications for research into embodiment of brain-computer interface controlled robotic prostheses and computer-generated limbs in virtual reality.

RevDate: 2023-03-17

Hou Y, Ling Y, Wang Y, et al (2023)

Learning from the Brain: Bioinspired Nanofluidics.

The journal of physical chemistry letters [Epub ahead of print].

The human brain completes intelligent behaviors such as the generation, transmission, and storage of neural signals by regulating the ionic conductivity of ion channels in neuron cells, which provides new inspiration for the development of ion-based brain-like intelligence. Against the backdrop of the gradual maturity of neuroscience, computer science, and micronano materials science, bioinspired nanofluidic iontronics, as an emerging interdisciplinary subject that focuses on the regulation of ionic conductivity of nanofluidic systems to realize brain-like functionalities, has attracted the attention of many researchers. This Perspective provides brief background information and the state-of-the-art progress of nanofluidic intelligent systems. Two main categories are included: nanofluidic transistors and nanofluidic memristors. The prospects of nanofluidic iontronics' interdisciplinary progress in future artificial intelligence fields such as neuromorphic computing or brain-computer interfaces are discussed. This Perspective aims to give readers a clear understanding of the concepts and prospects of this emerging interdisciplinary field.

RevDate: 2023-03-18

Carino-Escobar RI, Rodríguez-García ME, Carrillo-Mora P, et al (2023)

Continuous versus discrete robotic feedback for brain-computer interfaces aimed for neurorehabilitation.

Frontiers in neurorobotics, 17:1015464.

INTRODUCTION: Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.

METHODS: An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback.

RESULTS: Significantly higher BCI performance (65.4 ± 17.9% with the continuous and 62.1 ± 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback.

DISCUSSION: It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.

RevDate: 2023-03-16

Wei L, Qi X, Yu X, et al (2023)

3,4-Dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide analogs act as potential AMPA receptor potentiators with antidepressant activity.

European journal of medicinal chemistry, 251:115252 pii:S0223-5234(23)00218-0 [Epub ahead of print].

Major depressive disorder is a common psychiatric disorder, with ∼30% of patients suffering from treatment-resistant depression. Based on preclinical studies on ketamine, α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) activation may be a promising therapeutic approach. In this study, we synthesized a series of novel 3,4-dihydrobenzo[e][1,2,3]oxathiazine 2,2-dioxide analogs and analyzed their potential as AMPAR potentiators. Compounds 5aa and 7k exhibited high potentiation with little agonist activity in a high-throughput screen using a calcium influx assay in cultured hippocampal primary neurons. In rats, compound 7k had better pharmacokinetic properties and oral bioavailability (F = 67.19%); it also exhibited an acceptable safety profile in vital internal organs based on hematoxylin and eosin staining. We found that 7k produced a rapid antidepressant-like effect in chronic restraint stress-induced mice 1 h after intraperitoneal administration. Our study presented a series of novel AMPAR potentiators and identified 7k as a promising drug-like candidate against major depressive disorders.

RevDate: 2023-03-16

Kong L, Zhang D, Huang S, et al (2023)

Extracellular Vesicles in Mental Disorders: A State-of-art Review.

International journal of biological sciences, 19(4):1094-1109.

Extracellular vesicles (EVs) are nanoscale particles with various physiological functions including mediating cellular communication in the central nervous system (CNS), which indicates a linkage between these particles and mental disorders such as schizophrenia, bipolar disorder, major depressive disorder, etc. To date, known characteristics of mental disorders are mainly neuroinflammation and dysfunctions of homeostasis in the CNS, and EVs are proven to be able to regulate these pathological processes. In addition, studies have found that some cargo of EVs, especially miRNAs, were significantly up- or down-regulated in patients with mental disorders. For many years, interest has been generated in exploring new diagnostic and therapeutic methods for mental disorders, but scale assessment and routine drug intervention are still the first-line applications so far. Therefore, underlying the downstream functions of EVs and their cargo may help uncover the pathogenetic mechanisms of mental disorders as well as provide novel biomarkers and therapeutic candidates. This review aims to address the connection between EVs and mental disorders, and discuss the current strategies that focus on EVs-related psychiatric detection and therapy.

RevDate: 2023-03-16

Khodaei F, Sadati SH, Doost M, et al (2023)

LFP polarity changes across cortical and eccentricity in primary visual cortex.

Frontiers in neuroscience, 17:1138602.

Local field potentials (LFPs) can evaluate neural population activity in the cortex and their interaction with other cortical areas. Analyzing current source density (CSD) rather than LFPs is very significant due to the reduction of volume conduction effects. Current sinks are construed as net inward transmembrane currents, while current sources are net outward ones. Despite extensive studies of LFPs and CSDs, their morphology in different cortical layers and eccentricities are still largely unknown. Because LFP polarity changes provide a measure of neural activity, they can be useful in implanting brain-computer interface (BCI) chips and effectively communicating the BCI devices to the brain. We hypothesize that sinks and sources analyses could be a way to quantitatively achieve their characteristics in response to changes in stimulus size and layer-dependent differences with increasing eccentricities. In this study, we show that stimulus properties play a crucial role in determining the flow. The present work focusses on the primary visual cortex (V1). In this study, we investigate a map of the LFP-CSD in V1 area by presenting different stimulus properties (e.g., size and type) in the visual field area of Macaque monkeys. Our aim is to use the morphology of sinks and sources to measure the input and output information in different layers as well as different eccentricities. According to the value of CSDs, the results show that the stimuli smaller than RF's size had lower strength than the others and the larger RF's stimulus size showed smaller strength than the optimized stimulus size, which indicated the suppression phenomenon. Additionally, with the increased eccentricity, CSD's strengths were increased across cortical layers.

RevDate: 2023-03-15

Rokai J, Ulbert I, G Márton (2023)

Edge computing on TPU for brain implant signal analysis.

Neural networks : the official journal of the International Neural Network Society, 162:212-224 pii:S0893-6080(23)00108-9 [Epub ahead of print].

The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain-computer interfaces.

RevDate: 2023-03-17

Lehnertz K, Rings T, T Bröhl (2021)

Time in Brain: How Biological Rhythms Impact on EEG Signals and on EEG-Derived Brain Networks.

Frontiers in network physiology, 1:755016.

Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.

RevDate: 2023-03-15

Heerspink HJL, Inker LA, Tighiouart H, et al (2023)

Change in Albuminuria and GFR Slope as Joint Surrogate Endpoints for Kidney Failure: Implications for Phase 2 Clinical Trials in CKD.

Journal of the American Society of Nephrology : JASN pii:00001751-990000000-00103 [Epub ahead of print].

BACKGROUND: Change in log urinary albumin to creatinine ratio (UACR) and GFR slope are individually used as surrogate endpoints in clinical trials of CKD progression. Whether combining these surrogate endpoints might strengthen inferences about clinical benefit is unknown.

METHODS: Using Bayesian meta-regressions across 41 randomized trials of CKD progression, we characterized the combined relationship between the treatment effects on the clinical endpoint (sustained doubling of serum creatinine, GFR<15 ml/min per 1.73m2, or kidney failure) and treatment effects on UACR change and chronic GFR slope after 3 months. We applied the results to the design of phase 2 trials based on UACR change and chronic GFR slope in combination.

RESULTS: Treatment effects on the clinical endpoint were strongly associated with the combination of treatment effects on UACR change and chronic slope. The posterior median meta-regression coefficients for treatment effects were -0.41 (95% Baysian confidence interval [BCI], -0.64 to -0.17) per 1 ml/min per 1.73m2 per year for the treatment effect on GFR slope and -0.06 (95% BCI, -0.90 to 0.77) for the treatment effect on UACR change. The predicted probability of clinical benefit when considering both surrogates was determined primarily by estimated treatment effects on UACR when sample size was small (approximately 60 patients per treatment arm) and follow-up brief (approximately 1 year), with the importance of GFR slope increasing for larger sample sizes and longer follow-up.

CONCLUSIONS: In phase 2 trials of CKD with sample sizes of 100 to 200 patients per arm and follow-up between 1 and 2 years, combining information from treatment effects on UACR change and GFR slope improved prediction of treatment effects on clinical endpoints.

RevDate: 2023-03-15

Zhu Y, Wu L, Ye S, et al (2023)

The Chinese Version of Oxford Depression Questionnaire: A Validation Study in Patients with Mood Disorders.

Neuropsychiatric disease and treatment, 19:547-556.

BACKGROUND: Emotional blunting is prevalent in patients with mood disorders and adversely affects the overall treatment outcome. The Oxford Depression Questionnaire is a validated psychometric instrument for assessing emotional blunting. We aimed to evaluate the reliability and validity of the Chinese version of the ODQ (ODQ) in Chinese patients with mood disorders.

METHODS: 136 mood disorders patients and 95 healthy control participants were recruited at the First Affiliated Hospital of Zhejiang University, School of Medicine. Patients were assessed using the ODQ, Beck Depression Inventory-II (BDI-II), and Montgomery-Asberg Depression Rating Scale (MADRS). Internal consistency reliability and test-retest reliability were analyzed. Confirmatory factor analysis and correlation analysis were used to evaluate construct and convergent validity.

RESULTS: A total of 136 patients with mood disorders and 95 healthy controls participated in this study. Cronbach α values were 0.928 (ODQ-20) and 0.945 (ODQ-26). Test-retest reliability coefficients were 0.798 (ODQ-20) and 0.836 (ODQ-26) (p<0.05); intraclass correlation coefficient values were 0.777 (ODQ-20) and 0.781 (ODQ-26) (p<0.01). The score of ODQ was positively correlated with BDI-II and MADRS (r=0.326~0.719, 0.235~0.537, p<0.01). The differences in the ODQ scores between the patient and control groups were statistically significant.

CONCLUSION: The reliability, structural validity, and criterion validity of the ODQ applied to patients with mood disorders meet the psychometric requirements, and the scale can be used to assess emotional blunting in Chinese patients with mood disorders.

RevDate: 2023-03-15

McDermott EJ, Metsomaa J, Belardinelli P, et al (2023)

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.

Virtual reality, 27(1):347-369.

Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) presents further opportunities to improve therapeutic efficacy by personalizing the paradigm. Specifically, the idea is to synchronize the choice and timing of stimuli in the perceived virtual world with fluctuating brain states relevant to motor behavior. Here, we present an open source EEG single-trial based classification pipeline that is designed to identify ongoing brain states predictive of the planning and execution of movements. 9 healthy volunteers each performed 1080 trials of a repetitive reaching task with an implicit two-alternative forced choice, i.e., use of the right or left hand, in response to the appearance of a visual target. The performance of the EEG decoding pipeline was assessed with respect to classification accuracy of right vs. left arm use, based on the EEG signal at the time of the stimulus. Different features, feature extraction methods, and classifiers were compared at different time windows; the number and location of informative EEG channels and the number of calibration trials needed were also quantified, as well as any benefits from individual-level optimization of pipeline parameters. This resulted in a set of recommended parameters that achieved an average 83.3% correct prediction on never-before-seen testing data, and a state-of-the-art 77.1% in a real-time simulation. Neurophysiological plausibility of the resulting classifiers was assessed by time-frequency and event-related potential analyses, as well as by Independent Component Analysis topographies and cortical source localization. We expect that this pipeline will facilitate the identification of relevant brain states as prospective therapeutic targets in closed-loop EEG-VR motor neurorehabilitation.

RevDate: 2023-03-13

Dougherty LL, Dutta S, P Avasthi (2023)

The ERK activator, BCI, inhibits ciliogenesis and causes defects in motor behavior, ciliary gating, and cytoskeletal rearrangement.

Life science alliance, 6(6): pii:6/6/e202301899.

MAPK pathways are well-known regulators of the cell cycle, but they have also been found to control ciliary length in a wide variety of organisms and cell types from Caenorhabditis elegans neurons to mammalian photoreceptors through unknown mechanisms. ERK1/2 is a MAP kinase in human cells that is predominantly phosphorylated by MEK1/2 and dephosphorylated by the phosphatase DUSP6. We have found that the ERK1/2 activator/DUSP6 inhibitor, (E)-2-benzylidene-3-(cyclohexylamino)-2,3-dihydro-1H-inden-1-one (BCI), inhibits ciliary maintenance in Chlamydomonas and hTERT-RPE1 cells and assembly in Chlamydomonas These effects involve inhibition of total protein synthesis, microtubule organization, membrane trafficking, and KAP-GFP motor dynamics. Our data provide evidence for various avenues for BCI-induced ciliary shortening and impaired ciliogenesis that gives mechanistic insight into how MAP kinases can regulate ciliary length.

RevDate: 2023-03-13

Zhang Y, Zeng H, Lou F, et al (2023)

SLC45A3 Serves as a Potential Therapeutic Biomarker to Attenuate White Matter Injury After Intracerebral Hemorrhage.

Translational stroke research [Epub ahead of print].

Intracerebral hemorrhage (ICH) is a severe cerebrovascular disease, which impairs patients' white matter even after timely clinical interventions. Indicated by studies in the past decade, ICH-induced white matter injury (WMI) is closely related to neurological deficits; however, its underlying mechanism and pertinent treatment are yet insufficient. We gathered two datasets (GSE24265 and GSE125512), and by taking an intersection among interesting genes identified by weighted gene co-expression networks analysis, we determined target genes after differentially expressing genes in two datasets. Additional single-cell RNA-seq analysis (GSE167593) helped locate the gene in cell types. Furthermore, we established ICH mice models induced by autologous blood or collagenase. Basic medical experiments and diffusion tensor imaging were applied to verify the function of target genes in WMI after ICH. Through intersection and enrichment analysis, gene SLC45A3 was identified as the target one, which plays a key role in the regulation of oligodendrocyte differentiation involving in fatty acid metabolic process, etc. after ICH, and single-cell RNA-seq analysis also shows that it mainly locates in oligodendrocytes. Further experiments verified overexpression of SLC45A3 ameliorated brain injury after ICH. Therefore, SLC45A3 might serve as a candidate therapeutic biomarker for ICH-induced WMI, and overexpression of it may be a potential approach for injury attenuation.

RevDate: 2023-03-13

Tao QQ, Lin RR, ZY Wu (2023)

Early Diagnosis of Alzheimer's Disease: Moving Toward a Blood-Based Biomarkers Era.

Clinical interventions in aging, 18:353-358.

RevDate: 2023-03-13

Prescott RA, Pankow AP, de Vries M, et al (2023)

A comparative study of in vitro air-liquid interface culture models of the human airway epithelium evaluating cellular heterogeneity and gene expression at single cell resolution.

bioRxiv : the preprint server for biology pii:2023.02.27.530299.

The airway epithelium is composed of diverse cell types with specialized functions that mediate homeostasis and protect against respiratory pathogens. Human airway epithelial cultures at air-liquid interface (HAE) are a physiologically relevant in vitro model of this heterogeneous tissue, enabling numerous studies of airway disease [1â€"7] . HAE cultures are classically derived from primary epithelial cells, the relatively limited passage capacity of which can limit experimental methods and study designs. BCi-NS1.1, a previously described and widely used basal cell line engineered to express hTERT, exhibits extended passage lifespan while retaining capacity for differentiation to HAE [5] . However, gene expression and innate immune function in HAE derived from BCi-NS1.1 versus primary cells have not been fully characterized. Here, combining single cell RNA-Seq (scRNA-Seq), immunohistochemistry, and functional experimentation, we confirm at high resolution that BCi-NS1.1 and primary HAE cultures are largely similar in morphology, cell type composition, and overall transcriptional patterns. While we observed cell-type specific expression differences of several interferon stimulated genes in BCi-NS1.1 HAE cultures, we did not observe significant differences in susceptibility to infection with influenza A virus and Staphylococcus aureus . Taken together, our results further support BCi-NS1.1-derived HAE cultures as a valuable tool for the study of airway infectious disease.

RevDate: 2023-03-13

Du P, Li P, Cheng L, et al (2023)

Single-trial P300 classification algorithm based on centralized multi-person data fusion CNN.

Frontiers in neuroscience, 17:1132290.

INTRODUCTION: Currently, it is still a challenge to detect single-trial P300 from electroencephalography (EEG) signals. In this paper, to address the typical problems faced by existing single-trial P300 classification, such as complex, time-consuming and low accuracy processes, a single-trial P300 classification algorithm based on multiplayer data fusion convolutional neural network (CNN) is proposed to construct a centralized collaborative brain-computer interfaces (cBCI) for fast and highly accurate classification of P300 EEG signals.

METHODS: In this paper, two multi-person data fusion methods (parallel data fusion and serial data fusion) are used in the data pre-processing stage to fuse multi-person EEG information stimulated by the same task instructions, and then the fused data is fed as input to the CNN for classification. In building the CNN network for single-trial P300 classification, the Conv layer was first used to extract the features of single-trial P300, and then the Maxpooling layer was used to connect the Flatten layer for secondary feature extraction and dimensionality reduction, thereby simplifying the computation. Finally batch normalisation is used to train small batches of data in order to better generalize the network and speed up single-trial P300 signal classification.

RESULTS: In this paper, the above new algorithms were tested on the Kaggle dataset and the Brain-Computer Interface (BCI) Competition III dataset, and by analyzing the P300 waveform features and EEG topography and the four standard evaluation metrics, namely Accuracy, Precision, Recall and F1-score,it was demonstrated that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed other classification algorithms.

DISCUSSION: The results show that the single-trial P300 classification algorithm after two multi-person data fusion CNNs significantly outperformed the single-person model, and that the single-trial P300 classification algorithm with two multi-person data fusion CNNs involves smaller models, fewer training parameters, higher classification accuracy and improves the overall P300-cBCI classification rate and actual performance more effectively with a small amount of sample information compared to other algorithms.

RevDate: 2023-03-13

Zhang R, Chen Y, Xu Z, et al (2023)

Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.

Frontiers in neuroscience, 17:1129049.

Motor imagery-based brain-computer interfaces (MI-BCI) have important application values in the field of neurorehabilitation and robot control. At present, MI-BCI mostly use bilateral upper limb motor tasks, but there are relatively few studies on single upper limb MI tasks. In this work, we conducted studies on the recognition of motor imagery EEG signals of the right upper limb and proposed a multi-branch fusion convolutional neural network (MF-CNN) for learning the features of the raw EEG signals as well as the two-dimensional time-frequency maps at the same time. The dataset used in this study contained three types of motor imagery tasks: extending the arm, rotating the wrist, and grasping the object, 25 subjects were included. In the binary classification experiment between the grasping object and the arm-extending tasks, MF-CNN achieved an average classification accuracy of 78.52% and kappa value of 0.57. When all three tasks were used for classification, the accuracy and kappa value were 57.06% and 0.36, respectively. The comparison results showed that the classification performance of MF-CNN is higher than that of single CNN branch algorithms in both binary-class and three-class classification. In conclusion, MF-CNN makes full use of the time-domain and frequency-domain features of EEG, can improve the decoding accuracy of single limb motor imagery tasks, and it contributes to the application of MI-BCI in motor function rehabilitation training after stroke.

RevDate: 2023-03-13

Zuccaroli I, Lucke-Wold B, Palla A, et al (2023)

Neural Bypasses: Literature Review and Future Directions in Developing Artificial Neural Connections.

OBM neurobiology, 7(1):.

Reported neuro-modulation schemes in the literature are typically classified as closed-loop or open-loop. A novel group of recently developed neuro-modulation devices may be better described as a neural bypass, which attempts to transmit neural data from one location of the nervous system to another. The most common form of neural bypasses in the literature utilize EEG recordings of cortical information paired with functional electrical stimulation for effector muscle output, most commonly for assistive applications and rehabilitation in spinal cord injury or stroke. Other neural bypass locations that have also been described, or may soon be in development, include cortical-spinal bypasses, cortical-cortical bypasses, autonomic bypasses, peripheral-central bypasses, and inter-subject bypasses. The most common recording devices include EEG, ECoG, and microelectrode arrays, while stimulation devices include both invasive and noninvasive electrodes. Several devices are in development to improve the temporal and spatial resolution and biocompatibility for neuronal recording and stimulation. A major barrier to entry includes neuroplasticity and current decoding mechanisms that regularly require retraining. Neural bypasses are a unique class of neuro-modulation. Continued advancement of neural recording and stimulating devices with high spatial and temporal resolution, combined with decoding mechanisms uninhibited by neuroplasticity, can expand the therapeutic capability of neural bypassing. Overall, neural bypasses are a promising modality to improve the treatment of common neurologic disorders, including stroke, spinal cord injury, peripheral nerve injury, brain injury and more.

RevDate: 2023-03-12

Gavaret M, Iftimovici A, E Pruvost-Robieux (2023)

EEG: Current relevance and promising quantitative analyses.

Revue neurologique pii:S0035-3787(23)00869-X [Epub ahead of print].

Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.

RevDate: 2023-03-13

Tao T, Gao Y, Jia Y, et al (2023)

A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images.

Sensors (Basel, Switzerland), 23(5):.

An error-related potential (ErrP) occurs when people's expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain-computer interfaces.

RevDate: 2023-03-13

Huggins JE, Krusienski D, Vansteensel MJ, et al (2022)

Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.

Brain computer interfaces (Abingdon, England), 9(2):69-101.

The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9th, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering topics in BCI (also called brain-machine interface) research. As in the past, workshops covered the breadth of topics in BCI. Some workshops provided detailed examinations of specific methods, hardware, or processes. Others focused on specific BCI applications or user groups. Several workshops continued consensus building efforts designed to create BCI standards and increase the ease of comparisons between studies and the potential for meta-analysis and large multi-site clinical trials. Ethical and translational considerations were both the primary topic for some workshops or an important secondary consideration for others. The range of BCI applications continues to expand, with more workshops focusing on approaches that can extend beyond the needs of those with physical impairments. This paper summarizes each workshop, provides background information and references for further study, presents an overview of the discussion topics, and describes the conclusion, challenges, or initiatives that resulted from the interactions and discussion at the workshop.

RevDate: 2023-03-11

Saibene A, Caglioni M, Corchs S, et al (2023)

EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.

Sensors (Basel, Switzerland), 23(5): pii:s23052798.

In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

RevDate: 2023-03-11

Collazos-Huertas DF, Álvarez-Meza AM, Cárdenas-Peña DA, et al (2023)

Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity.

Sensors (Basel, Switzerland), 23(5): pii:s23052750.

Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of "poor skill" subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.

RevDate: 2023-03-11

Oikonomou VP, Georgiadis K, Kalaganis F, et al (2023)

A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing.

Sensors (Basel, Switzerland), 23(5): pii:s23052480.

In this work, we propose a novel framework to recognize the cognitive and affective processes of the brain during neuromarketing-based stimuli using EEG signals. The most crucial component of our approach is the proposed classification algorithm that is based on a sparse representation classification scheme. The basic assumption of our approach is that EEG features from a cognitive or affective process lie on a linear subspace. Hence, a test brain signal can be represented as a linear (or weighted) combination of brain signals from all classes in the training set. The class membership of the brain signals is determined by adopting the Sparse Bayesian Framework with graph-based priors over the weights of linear combination. Furthermore, the classification rule is constructed by using the residuals of linear combination. The experiments on a publicly available neuromarketing EEG dataset demonstrate the usefulness of our approach. For the two classification tasks offered by the employed dataset, namely affective state recognition and cognitive state recognition, the proposed classification scheme manages to achieve a higher classification accuracy compared to the baseline and state-of-the art methods (more than 8% improvement in classification accuracy).

RevDate: 2023-03-11

Oikonomou VP (2023)

Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals.

Sensors (Basel, Switzerland), 23(5): pii:s23052425.

Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we propose a novel approach by considering spatial patterns of the brain's responses due to visual stimulation at specific frequencies. More specifically, we propose, for the identification of the individuals, to combine common spatial patterns with specialized deep-learning neural networks. The adoption of common spatial patterns gives us the ability to design personalized spatial filters. In addition, with the help of deep neural networks, the spatial patterns are mapped into new (deep) representations where the discrimination between individuals is performed with a high correct recognition rate. We conducted a comprehensive comparison between the performance of the proposed method and several classical methods on two steady-state visual evoked potential datasets consisting of thirty-five and eleven subjects, respectively. Furthermore, our analysis includes a large number of flickering frequencies in the steady-state visual evoked potential experiment. Experiments on these two steady-state visual evoked potential datasets showed the usefulness of our approach in terms of person identification and usability. The proposed method achieved an averaged correct recognition rate of 99% over a large number of frequencies for the visual stimulus.

RevDate: 2023-03-11

Niu K, An Z, Yao Z, et al (2023)

Effects of Different Bedding Materials on Production Performance, Lying Behavior and Welfare of Dairy Buffaloes.

Animals : an open access journal from MDPI, 13(5): pii:ani13050842.

Different bedding materials have important effects on the behavioristics, production performance and welfare of buffalo. This study aimed to compare the effects of two bedding materials on lying behavior, production performance and animal welfare of dairy buffaloes. More than 40 multiparous lactating buffaloes were randomly divided into two groups, which were raised on fermented manure bedding (FMB) and chaff bedding (CB). The results showed that the application of FMB improved the lying behavior of buffaloes, the average daily lying time (ADLT) of buffaloes in FMB increased by 58 min compared to those in CB, with a significant difference (p < 0.05); the average daily standing time (ADST) decreased by 30 min, with a significant difference (p < 0.05); and the buffalo comfort index (BCI) increased, but the difference was not significant (p > 0.05). The average daily milk yield of buffaloes in FMB increased by 5.78% compared to buffaloes in CB. The application of FMB improved the hygiene of buffaloes. The locomotion score and hock lesion score were not significantly different between the two groups and all buffaloes did not show moderate and severe lameness. The price of FMB was calculated to be 46% of CB, which greatly reduced the cost of bedding material. In summary, FMB has significantly improved the lying behavior, production performance and welfare of buffaloes and significantly reduce the cost of bedding material.

RevDate: 2023-03-11

Zhao SN, Cui Y, He Y, et al (2023)

Teleoperation control of a wheeled mobile robot based on Brain-machine Interface.

Mathematical biosciences and engineering : MBE, 20(2):3638-3660.

This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.

RevDate: 2023-03-11

Yang L, Shi T, Lv J, et al (2023)

A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery.

Mathematical biosciences and engineering : MBE, 20(2):2482-2500.

To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.

RevDate: 2023-03-11

Gan L, Yin X, Huang J, et al (2023)

Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease.

Mathematical biosciences and engineering : MBE, 20(2):1695-1715.

Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute nonfatal event and is characterized by high morbidity, disability, and mortality. Transcranial Doppler (TCD) ultrasonography is a non-invasive method for the diagnosis of cerebrovascular disease that uses the Doppler effect to detect the hemodynamic and physiological parameters of the major intracranial basilar arteries. It can provide important hemodynamic information that cannot be measured by other diagnostic imaging techniques for cerebrovascular disease. And the result parameters of TCD ultrasonography such as blood flow velocity and beat index can reflect the type of cerebrovascular disease and serve as a basis to assist physicians in the treatment of cerebrovascular diseases. Artificial intelligence (AI) is a branch of computer science which is used in a wide range of applications in agriculture, communications, medicine, finance, and other fields. In recent years, there are much research devoted to the application of AI to TCD. The review and summary of related technologies is an important work to promote the development of this field, which can provide an intuitive technical summary for future researchers. In this paper, we first review the development, principles, and applications of TCD ultrasonography and other related knowledge, and briefly introduce the development of AI in the field of medicine and emergency medicine. Finally, we summarize in detail the applications and advantages of AI technology in TCD ultrasonography including the establishment of an examination system combining brain computer interface (BCI) and TCD ultrasonography, the classification and noise cancellation of TCD ultrasonography signals using AI algorithms, and the use of intelligent robots to assist physicians in TCD ultrasonography and discuss the prospects for the development of AI in TCD ultrasonography.

RevDate: 2023-03-10

Wu Z, She Q, Hou Z, et al (2023)

Multi-source online transfer algorithm based on source domain selection for EEG classification.

Mathematical biosciences and engineering : MBE, 20(3):4560-4573.

The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.

RevDate: 2023-03-08

Greenwell D, Vanderkolff S, J Feigh (2023)

Understanding De Novo Learning for Brain Machine Interfaces.

Journal of neurophysiology [Epub ahead of print].

De novo motor learning is a form of motor learning characterized by the development of an entirely new and distinct motor controller to accommodate a novel motor demand. Inversely, adaptation is a form of motor learning characterized by rapid, unconscious modifications in a previously established motor controller to accommodate small deviations in task demands. Since most of motor learning involves the adaption of previously established motor controllers, de novo learning can be challenging to isolate and observe. The recent publication from Haith et al. (2022) details a novel method to investigate de novo learning using a complex bimanual cursor control task. This research is especially important in the context of future brain machine interface devices that will present users with an entirely novel motor learning demand, requiring de novo learning.

RevDate: 2023-03-06

Mathon B, Navarro V, Lecas S, et al (2023)

Safety Profile of Low-Intensity Pulsed Ultrasound-Induced Blood-Brain Barrier Opening in Non-epileptic Mice and in a Mouse Model of Mesial Temporal Lobe Epilepsy.

Ultrasound in medicine & biology pii:S0301-5629(23)00039-X [Epub ahead of print].

OBJECTIVE: It is unknown whether ultrasound-induced blood-brain barrier (BBB) disruption can promote epileptogenesis and how BBB integrity changes over time after sonication.

METHODS: To gain more insight into the safety profile of ultrasound (US)-induced BBB opening, we determined BBB permeability as well as histological modifications in C57BL/6 adult control mice and in the kainate (KA) model for mesial temporal lobe epilepsy in mice after sonication with low-intensity pulsed ultrasound (LIPU). Microglial and astroglial changes in ipsilateral hippocampus were examined at different time points following BBB disruption by respectively analyzing Iba1 and glial fibrillary acidic protein immunoreactivity. Using intracerebral EEG recordings, we further studied the possible electrophysiological repercussions of a repeated disrupted BBB for seizure generation in nine non-epileptic mice.

RESULTS: LIPU-induced BBB opening led to transient albumin extravasation and reversible mild astrogliosis, but not to microglial activation in the hippocampus of non-epileptic mice. In KA mice, the transient albumin extravasation into the hippocampus mediated by LIPU-induced BBB opening did not aggravate inflammatory processes and histologic changes that characterize the hippocampal sclerosis. Three LIPU-induced BBB opening did not induce epileptogenicity in non-epileptic mice implanted with depth EEG electrodes.

CONCLUSION: Our experiments in mice provide persuasive evidence of the safety of LIPU-induced BBB opening as a therapeutic modality for neurological diseases.

RevDate: 2023-03-06

Ma J, Hu Z, Yue H, et al (2023)

GRM2 regulates functional integration of adult-born DGCs by paradoxically modulating MEK/ERK1/2 pathway.

The Journal of neuroscience : the official journal of the Society for Neuroscience pii:JNEUROSCI.1886-22.2023 [Epub ahead of print].

Metabotropic glutamate receptor 2 (GRM2) is highly expressed in hippocampal dentate granule cells (DGCs), regulating synaptic transmission and hippocampal functions. Newborn DGCs are continuously generated throughout life, and express GRM2 when they are mature. However, it remained unclear whether and how GRM2 regulates the development and integration of these newborn neurons. We discovered that the expression of GRM2 in adult-born DGCs increased with neuronal development in mice of both sexes. Lack of GRM2 caused developmental defects of DGCs and impaired hippocampus-dependent cognitive functions. Intriguingly, our data showed that knockdown of Grm2 resulted in decreased b/c-Raf kinases, and paradoxically led to an excessive activation of MEK/ERK1/2 pathway. Inhibition of MEK ameliorated the developmental defects caused by Grm2 knockdown. Together, our results indicate that GRM2 is necessary for the development and functional integration of newborn DGCs in the adult hippocampus through regulating the phosphorylation and activation state of MEK/ERK1/2 pathway.SIGNIFICANCE STATEMENT:Metabotropic glutamate receptor 2 (GRM2) is highly expressed in mature dentate granule cells (DGCs) in the hippocampus. It remains unclear whether GRM2 is required for the development and integration of adult-born DGCs. We provided a series of in vivo and in vitro evidence to show GRM2 regulates the development of adult-born DGCs and their integration into existing hippocampal circuits. Lack of GRM2 in a cohort of newborn DGCs impaired object-to-location memory in mice. Moreover, we revealed that GRM2 knockdown paradoxically upregulated MEK/ERK1/2 pathway by suppressing b/c-Raf in developing neurons, which is likely a common mechanism underlying the regulation of the development of neurons expressing GRM2. Thus, Raf/MEK/ERK1/2 pathway could be a potential target for brain diseases related to GRM2 abnormality.

RevDate: 2023-03-06

Qi HX, Reed JL, Liao CC, et al (2023)

Regressive changes in sizes of somatosensory cuneate nucleus after sensory loss in primates.

Proceedings of the National Academy of Sciences of the United States of America, 120(11):e2222076120.

Neurons in the early stages of processing sensory information suffer transneuronal atrophy when deprived of their activating inputs. For over 40 y, members of our laboratory have studied the reorganization of the somatosensory cortex during and after recovering from different types of sensory loss. Here, we took advantage of the preserved histological material from these studies of the cortical effects of sensory loss to evaluate the histological consequences in the cuneate nucleus of the lower brainstem and the adjoining spinal cord. The neurons in the cuneate nucleus are activated by touch on the hand and arm, and relay this activation to the contralateral thalamus, and from the thalamus to the primary somatosensory cortex. Neurons deprived of activating inputs tend to shrink and sometimes die. We considered the effects of differences in species, type and extent of sensory loss, recovery time after injury, and age at the time of injury on the histology of the cuneate nucleus. The results indicate that all injuries that deprived part or all of the cuneate nucleus of sensory activation result in some atrophy of neurons as reflected by a decrease in nucleus size. The extent of the atrophy is greater with greater sensory loss and with longer recovery times. Based on supporting research, atrophy appears to involve a reduction in neuron size and neuropil, with little or no neuron loss. Thus, the potential exists for restoring the hand to cortex pathway with brain-machine interfaces, for bionic prosthetics, or biologically with hand replacement surgery.

RevDate: 2023-03-06

He C, S Duan (2023)

Novel Insight into Glial Biology and Diseases.

RevDate: 2023-03-06

Kumar A, Sah DK, Khanna K, et al (2023)

A calcium and zinc composite alginate hydrogel for pre-hospital hemostasis and wound care.

Carbohydrate polymers, 299:120186.

We developed, characterized, and examined the hemostatic potential of sodium alginate-based Ca[2+] and Zn[2+] composite hydrogel (SA-CZ). SA-CZ hydrogel showed substantial in-vitro efficacy, as observed by the significant reduction in coagulation time with better blood coagulation index (BCI) and no evident hemolysis in human blood. SA-CZ significantly reduced bleeding time (≈60 %) and mean blood loss (≈65 %) in the tail bleeding and liver incision in the mice hemorrhage model (p ≤ 0.001). SA-CZ also showed enhanced cellular migration (1.58-fold) in-vitro and improved wound closure (≈70 %) as compared with betadine (≈38 %) and saline (≈34 %) at the 7th-day post-wound creation in-vivo (p < 0.005). Subcutaneous implantation and intra-venous gamma-scintigraphy of hydrogel revealed ample body clearance and non-considerable accumulation in any vital organ, proving its non-thromboembolic nature. Overall, SA-CZ showed good biocompatibility along with efficient hemostasis and wound healing qualities, making it suitable as a safe and effective aid for bleeding wounds.

RevDate: 2023-03-06

Porcaro C, Avanaki K, Arias-Carrion O, et al (2023)

Editorial: Combined EEG in research and diagnostics: Novel perspectives and improvements.

Frontiers in neuroscience, 17:1152394.

RevDate: 2023-03-06

Zhang J, Gao S, Zhou K, et al (2023)

An online hybrid BCI combining SSVEP and EOG-based eye movements.

Frontiers in human neuroscience, 17:1103935.

Hybrid brain-computer interface (hBCI) refers to a system composed of a single-modality BCI and another system. In this paper, we propose an online hybrid BCI combining steady-state visual evoked potential (SSVEP) and eye movements to improve the performance of BCI systems. Twenty buttons corresponding to 20 characters are evenly distributed in the five regions of the GUI and flash at the same time to arouse SSVEP. At the end of the flash, the buttons in the four regions move in different directions, and the subject continues to stare at the target with eyes to generate the corresponding eye movements. The CCA method and FBCCA method were used to detect SSVEP, and the electrooculography (EOG) waveform was used to detect eye movements. Based on the EOG features, this paper proposes a decision-making method based on SSVEP and EOG, which can further improve the performance of the hybrid BCI system. Ten healthy students took part in our experiment, and the average accuracy and information transfer rate of the system were 94.75% and 108.63 bits/min, respectively.

RevDate: 2023-03-03

Wang Y, Lin J, Li J, et al (2023)

Chronic neuronal inactivity utilizes the mTOR-TFEB pathway to drive transcription-dependent autophagy for homeostatic up-scaling.

The Journal of neuroscience : the official journal of the Society for Neuroscience pii:JNEUROSCI.0146-23.2023 [Epub ahead of print].

Activity-dependent changes in protein expression are critical for neuronal plasticity, a fundamental process for the processing and storage of information in the brain. Among the various forms of plasticity, homeostatic synaptic up-scaling is unique in that it is induced primarily by neuronal inactivity. However, precisely how the turnover of synaptic proteins occurs in this homeostatic process remains unclear. Here, we report that chronically inhibiting neuronal activity in primary cortical neurons prepared from E18 Sprague-Dawley rats (both sexes) induces autophagy, thereby regulating key synaptic proteins for up-scaling. Mechanistically, chronic neuronal inactivity causes dephosphorylation of ERK and mTOR, which induces TFEB-mediated cytonuclear signaling and drives transcription-dependent autophagy to regulate αCaMKII and PSD95 during synaptic up-scaling. Together, these findings suggest that mTOR-dependent autophagy, which is often triggered by metabolic stressors such as starvation, is recruited and sustained during neuronal inactivity in order to maintain synaptic homeostasis, a process that ensures proper brain function and if impaired can cause neuropsychiatric disorders such as autism.SIGNIFICANCE STATEMENT:In the mammalian brain, protein turnover is tightly controlled by neuronal activation to ensure key neuronal functions during long-lasting synaptic plasticity. However, a long-standing question is how this process occurs during synaptic up-scaling, a process that requires protein turnover but is induced by neuronal inactivation. Here, we report that mTOR-dependent signaling-which is often triggered by metabolic stressors such as starvation-is "hijacked" by chronic neuronal inactivation, which then serves as a nucleation point for TFEB cytonuclear signaling that drives transcription-dependent autophagy for up-scaling. These results provide the first evidence of a physiological role of mTOR-dependent autophagy in enduing neuronal plasticity, thereby connecting major themes in cell biology and neuroscience via a servo loop that mediates autoregulation in the brain.

RevDate: 2023-03-03

Yang Y, Zhang F, Gao X, et al (2023)

Progressive alterations in electrophysiological and epileptic network properties during the development of temporal lobe epilepsy in rats.

Epilepsy & behavior : E&B, 141:109120 pii:S1525-5050(23)00038-0 [Epub ahead of print].

OBJECTIVE: Refractory temporal lobe epilepsy (TLE) with recurring seizures causing continuing pathological changes in neural reorganization. There is an incomplete understanding of how spatiotemporal electrophysiological characteristics changes during the development of TLE. Long-term multi-site epilepsy patients' data is hard to obtain. Thus, our study relied on animal models to reveal the changes in electrophysiological and epileptic network characteristics systematically.

METHODS: Long-term local field potentials (LFPs) were recorded over a period of 1 to 4 months from 6 pilocarpine-treated TLE rats. We compared variations of seizure onset zone (SOZ), seizure onset pattern (SOP), the latency of seizure onsets, and functional connectivity network from 10-channel LFPs between the early and late stages. Moreover, three machine learning classifiers trained by early-stage data were used to test seizure detection performance in the late stage.

RESULTS: Compared to the early stage, the earliest seizure onset was more frequently detected in hippocampus areas in the late stage. The latency of seizure onsets between electrodes became shorter. Low-voltage fast activity (LVFA) was the most common SOP and the proportion of it increased in the late stage. Different brain states were observed during seizures using Granger causality (GC). Moreover, seizure detection classifiers trained by early-stage data were less accurate when tested in late-stage data.

SIGNIFICANCE: Neuromodulation especially closed-loop deep brain stimulation (DBS) is effective in the treatment of refractory TLE. Although the frequency or amplitude of the stimulation is generally adjusted in existing closed-loop DBS devices in clinical usage, the adjustment rarely considers the pathological progression of chronic TLE. This suggests that an important factor affecting the therapeutic effect of neuromodulation may have been overlooked. The present study reveals time-varying electrophysiological and epileptic network properties in chronic TLE rats and indicates that classifiers of seizure detection and neuromodulation parameters might be designed to adapt to the current state dynamically with the progression of epilepsy.

RevDate: 2023-03-03

Chen S, Guan X, Xie L, et al (2023)

Aloe-emodin targets multiple signaling pathways by blocking ubiquitin-mediated degradation of DUSP1 in nasopharyngeal carcinoma cells.

Phytotherapy research : PTR [Epub ahead of print].

Aloe-emodin (AE) has been shown to inhibit the proliferation of several cancer cell lines, including human nasopharyngeal carcinoma (NPC) cell lines. In this study, we confirmed that AE inhibited malignant biological behaviors, including cell viability, abnormal proliferation, apoptosis, and migration of NPC cells. Western blotting analysis revealed that AE upregulated the expression of DUSP1, an endogenous inhibitor of multiple cancer-associated signaling pathways, resulting in blockage of the extracellular signal-regulated kinase (ERK)-1/2, protein kinase B (AKT), and p38-mitogen activated protein kinase(p38-MAPK) signaling pathways in NPC cell lines. Moreover, the selective inhibitor of DUSP1, BCI-hydrochloride, partially reversed the AE-induced cytotoxicity and blocked the aforementioned signaling pathways in NPC cells. In addition, the binding between AE and DUSP1 was predicted via molecular docking analysis using AutoDock-Vina software and further verified via a microscale thermophoresis assay. The binding amino acid residues were adjacent to the predicted ubiquitination site (Lys192) of DUSP1. Immunoprecipitation with the ubiquitin antibody, ubiquitinated DUSP1 was shown to be upregulated by AE. Our findings revealed that AE can stabilize DUSP1 by blocking its ubiquitin-proteasome-mediated degradation and proposed an underlying mechanism by which AE-upregulated DUSP1 may potentially target multiple pathways in NPC cells.

RevDate: 2023-03-03

Patel HH, Berlinberg EJ, Nwachukwu B, et al (2023)

Quadriceps Weakness is Associated with Neuroplastic Changes Within Specific Corticospinal Pathways and Brain Areas After Anterior Cruciate Ligament Reconstruction: Theoretical Utility of Motor Imagery-Based Brain-Computer Interface Technology for Rehabilitation.

Arthroscopy, sports medicine, and rehabilitation, 5(1):e207-e216.

UNLABELLED: Persistent quadriceps weakness is a problematic sequela of anterior cruciate ligament reconstruction (ACLR). The purposes of this review are to summarize neuroplastic changes after ACL reconstruction; provide an overview of a promising interventions, motor imagery (MI), and its utility in muscle activation; and propose a framework using a brain-computer interface (BCI) to augment quadriceps activation. A literature review of neuroplastic changes, MI training, and BCI-MI technology in postoperative neuromuscular rehabilitation was conducted in PubMed, Embase, and Scopus. Combinations of the following search terms were used to identify articles: "quadriceps muscle," "neurofeedback," "biofeedback," "muscle activation," "motor learning," "anterior cruciate ligament," and "cortical plasticity." We found that ACLR disrupts sensory input from the quadriceps, which results in reduced sensitivity to electrochemical neuronal signals, an increase in central inhibition of neurons regulating quadriceps control and dampening of reflexive motor activity. MI training consists of visualizing an action, without physically engaging in muscle activity. Imagined motor output during MI training increases the sensitivity and conductivity of corticospinal tracts emerging from the primary motor cortex, which helps "exercise" the connections between the brain and target muscle tissues. Motor rehabilitation studies using BCI-MI technology have demonstrated increased excitability of the motor cortex, corticospinal tract, spinal motor neurons, and disinhibition of inhibitory interneurons. This technology has been validated and successfully applied in the recovery of atrophied neuromuscular pathways in stroke patients but has yet to be investigated in peripheral neuromuscular insults, such as ACL injury and reconstruction. Well-designed clinical studies may assess the impact of BCI on clinical outcomes and recovery time. Quadriceps weakness is associated with neuroplastic changes within specific corticospinal pathways and brain areas. BCI-MI shows strong potential for facilitating recovery of atrophied neuromuscular pathways after ACLR and may offer an innovative, multidisciplinary approach to orthopaedic care.

LEVEL OF EVIDENCE: V, expert opinion.

RevDate: 2023-03-03

Forenzo D, Liu Y, Kim J, et al (2023)

Integrating simultaneous motor imagery and spatial attention for EEG-BCI control.

bioRxiv : the preprint server for biology pii:2023.02.20.529307.

OBJECTIVE: EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control.

METHODS: We evaluated a cohort of 25 human subjects' ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI).

RESULTS: Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), statistically outperforms MI alone (42%), and was higher, but not statistically significant, than OSA alone (45%). MI+OSA had a similar performance to each subject's best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA.

CONCLUSION: Integrating MI and OSA leads to improved performance over MI alone at the group level and is the best BCI paradigm option for some subjects.

SIGNIFICANCE: This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users' BCI performance.

RevDate: 2023-03-02

Li G, Liu Y, Chen Y, et al (2023)

Polyvinyl alcohol/polyacrylamide double-network hydrogel-based semi-dry electrodes for robust electroencephalography recording at hairy scalp for noninvasive brain-computer interfaces.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Reliable and user-friendly electrodes can continuously and real-time capture the electroencephalography signals, which is essential for real-life brain-computer interfaces (BCIs). This study develops a flexible, durable, and low-contact-impedance polyvinyl alcohol/polyacrylamide double-network hydrogel (PVA/PAM DNH)-based semi-dry electrode for robust electroencephalography recording at hairy scalp.

APPROACH: The PVA/PAM DNHs are developed using a cyclic freeze-thaw strategy and used as a saline reservoir for semi-dry electrodes. The PVA/PAM DNHs steadily deliver trace amounts of saline onto the scalp, enabling low and stable electrode-scalp impedance. The hydrogel also conforms well to the wet scalp, stabilizing the electrode-scalp interface. The feasibility of the real-life BCIs is validated by conducting four classic BCI paradigms on 16 participants.

MAIN RESULTS: The results show that the PVA/PAM DNHs with 7.5%wt% PVA achieve a satisfactory trade-off between the saline load-unloading capacity and the compressive strength. The proposed semi-dry electrode exhibits a low contact impedance (18 ± 8.9 kΩ at 10 Hz), a small offset potential (0.46 mV), and negligible potential drift (1.5 ± 0.4 μV/min). The temporal cross-correlation between the semi-dry and wet electrodes is 0.91, and the spectral coherence is higher than 0.90 at frequencies below 45 Hz. Furthermore, no significant differences are present in BCI classification accuracy between these two typical electrodes.

SIGNIFICANCE: Based on the durability, rapid setup, wear-comfort, and robust signals of the developed hydrogel, PVA/PAM DNH-based semi-dry electrodes are a promising alternative to wet electrodes in real-life BCIs. .

RevDate: 2023-03-03

Chen X, Ma R, Zhang W, et al (2023)

Alpha oscillatory activity is causally linked to working memory retention.

PLoS biology, 21(2):e3001999 pii:PBIOLOGY-D-22-00185.

Although previous studies have reported correlations between alpha oscillations and the "retention" subprocess of working memory (WM), causal evidence has been limited in human neuroscience due to the lack of delicate modulation of human brain oscillations. Conventional transcranial alternating current stimulation (tACS) is not suitable for demonstrating the causal evidence for parietal alpha oscillations in WM retention because of its inability to modulate brain oscillations within a short period (i.e., the retention subprocess). Here, we developed an online phase-corrected tACS system capable of precisely correcting for the phase differences between tACS and concurrent endogenous oscillations. This system permits the modulation of brain oscillations at the target stimulation frequency within a short stimulation period and is here applied to empirically demonstrate that parietal alpha oscillations causally relate to WM retention. Our experimental design included both in-phase and anti-phase alpha-tACS applied to participants during the retention subprocess of a modified Sternberg paradigm. Compared to in-phase alpha-tACS, anti-phase alpha-tACS decreased both WM performance and alpha activity. These findings strongly support a causal link between alpha oscillations and WM retention and illustrate the broad application prospects of phase-corrected tACS.

RevDate: 2023-03-02

Mao C, Xiao P, Tao XN, et al (2023)

Unsaturated bond recognition leads to biased signal in a fatty acid receptor.

Science (New York, N.Y.) [Epub ahead of print].

Individual free fatty acids (FAs) play important roles in metabolic homeostasis, many via engagement with more than 40 GPCRs. Searching for receptors to sense beneficial ω-3 FAs of fish oil enabled the identification of GPR120, involving with a spectrum of metabolic diseases. Here, we report six cryo-EM structures of GPR120 in complex with FA hormones or TUG891 and Gi or Giq trimers. Aromatic residues inside the GPR120 ligand pocket were responsible for recognizing different double-bond positions of these FAs and connect ligand recognition to distinct effector coupling. We also investigated synthetic ligand selectivity and the structural basis of missense single nucleotide polymorphisms. We reveal how GPR120 differentiates rigid double bonds and flexible single bonds and may facilitate rational drug design targeting to GPR120.

RevDate: 2023-03-02

Shah AM (2023)

New development in brain-computer interface platforms: 1-year results from the SWITCH trial.

Synchron publishes SWITCH trial results demonstrating the safety and efficacy of stentrode™ device. The stentrode™ is an endovascularly implanted brain-computer interface communication device capable of relaying neural activity from the motor cortex of paralyzed patients. The platform has been used to recover speech.

RevDate: 2023-03-02

Valeriani D, Cecotti H, Thelen A, et al (2023)

Editorial: Translational brain-computer interfaces: From research labs to the market and back.

Frontiers in human neuroscience, 17:1152466.

RevDate: 2023-03-02

Huang G, Zhao Z, Zhang S, et al (2023)

Discrepancy between inter- and intra-subject variability in EEG-based motor imagery brain-computer interface: Evidence from multiple perspectives.

Frontiers in neuroscience, 17:1122661.

INTRODUCTION: Inter- and intra-subject variability are caused by the variability of the psychological and neurophysiological factors over time and across subjects. In the application of in Brain-Computer Interfaces (BCI), the existence of inter- and intra-subject variability reduced the generalization ability of machine learning models seriously, which further limited the use of BCI in real life. Although many transfer learning methods can compensate for the inter- and intra-subject variability to some extent, there is still a lack of clear understanding about the change of feature distribution between the cross-subject and cross-session electroencephalography (EEG) signal.

METHODS: To investigate this issue, an online platform for motor-imagery BCI decoding has been built in this work. The EEG signal from both the multi-subject (Exp1) and multi-session (Exp2) experiments has been analyzed from multiple perspectives.

RESULTS: Firstly we found that with the similar variability of classification results, the time-frequency response of the EEG signal within-subject in Exp2 is more consistent than cross-subject results in Exp1. Secondly, the standard deviation of the common spatial pattern (CSP) feature has a significant difference between Exp1 and Exp2. Thirdly, for model training, different strategies for the training sample selection should be applied for the cross-subject and cross-session tasks.

DISCUSSION: All these findings have deepened the understanding of inter- and intra-subject variability. They can also guide practice for the new transfer learning methods development in EEG-based BCI. In addition, these results also proved that BCI inefficiency was not caused by the subject's unable to generate the event-related desynchronization/synchronization (ERD/ERS) signal during the motor imagery.

RevDate: 2023-03-02

Ivanov N, T Chau (2023)

Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training.

Frontiers in computational neuroscience, 17:1108889.

Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (classDistinct reflecting the degree of class separability and classStability reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.

RevDate: 2023-03-01

Song Y, Sun Z, Sun W, et al (2023)

Neuroplasticity Following Stroke from a Functional Laterality Perspective: A fNIRS Study.

Brain topography [Epub ahead of print].

To explore alterations of resting-state functional connectivity (rsFC) in sensorimotor cortex following strokes with left or right hemiplegia considering the lateralization and neuroplasticity. Seventy-three resting-state functional near-infrared spectroscopy (fNIRS) files were selected, including 26 from left hemiplegia (LH), 21 from right hemiplegia (RH) and 26 from normal controls (NC) group. Whole-brain analyses matching the Pearson correlation were used for rsFC calculations. For right-handed normal controls, rsFC of motor components (M1 and M2) in the left hemisphere displayed a prominent intensity in comparison with the right hemisphere (p < 0.05), while for stroke groups, this asymmetry has disappeared. Additionally, RH rather than LH showed stronger rsFC between left S1 and left M1 in contrast to normal controls (p < 0.05), which correlated inversely with motor function (r = - 0.53, p < 0.05). Regarding M1, rsFC within ipsi-lesioned M1 has a negative correlation with motor function of the affected limb (r = - 0.60 for the RH group and - 0.43 for the LH group, p < 0.05). The rsFC within contra-lesioned M1 that innervates the normal side was weakened compared with that of normal controls (p < 0.05). Stronger rsFC of motor components in left hemisphere was confirmed by rs-fNIRS as the "secret of dominance" for the first time, while post-stroke hemiplegia broke this cortical asymmetry. Meanwhile, a statistically strengthened rsFC between left S1 and M1 only in right-hemiplegia group may act as a compensation for the impairment of the dominant side. This research has implications for brain-computer interfaces synchronizing sensory feedback with motor performance and transcranial magnetic regulation for cortical excitability to induce cortical plasticity.

RevDate: 2023-03-01

Çevik Saldıran T, Kara İ, Dinçer E, et al (2023)

Cross-cultural adaptation and validation of Diabetes Quality of Life Brief Clinical Inventory in Turkish patients with type 2 diabetes mellitus.

Disability and rehabilitation [Epub ahead of print].

PURPOSE: To translate and culturally adapt the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) into Turkish and assess the psychometric properties of the translated version.

METHODS: A forward-backward translation process was conducted in conformity with international guidelines. A total of 150 patients with type 2 diabetes mellitus (T2DM) completed the Turkish version of DQoL-BCI (DQoL-BCI-Tr). The factor structure, test-retest reliability, and construct validity were evaluated.

RESULTS: In the DQoL-BCI-Tr, the three-factor structure was found optimal and explained 68.7% of the variance. The DQoL-BCI-Tr showed excellent internal consistency (Cronbach's alpha = 0.90) and test-retest reliability (ICC = 0.98). Cronbach's alpha values ranged from 0.85 to 0.91 for subscales (satisfaction, worry, impact). A negative correlation was found between the total scores of the DQoL-BCI-Tr and the EuroQoL-5 dimensions (EQ-5D) indexes (r= -0.22, p < 0.01). The DQoL-BCI-Tr total score and satisfaction and worry subscale scores differentiated between groups defined by glycated hemoglobin (HbA1c>9%) and the use of insulin.

CONCLUSIONS: The study results showed that the DQoL-BCI-Tr can be served as a reliable and valid instrument to obtain information from Turkish patients with T2DM diagnosis, including satisfaction with treatment, the impact of the disease, and worry about the social/vocational issues.Implications for rehabilitationThe Turkish version of the Diabetes Quality of Life Brief Clinical Inventory (DQoL-BCI) is a valid and reliable instrument.The DQoL-BCI Questionnaire in Turkish (DQoL-BCI-Tr) is an easy and quick way to determine satisfaction with treatment, impact of disease, and worry about the social/vocational issues.The DQoL-BCI-Tr is a reliable instrument for assessing disease-specific effects, emotional loads, and satisfaction of Turkish patients with type 2 diabetes in clinical and research settings.

RevDate: 2023-02-28

Zheng C, Liu Y, Xiao X, et al (2023)

[Advances in brain-computer interface based on high-frequency steady-state visual evoked potential].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 40(1):155-162.

Steady-state visual evoked potential (SSVEP) has been widely used in the research of brain-computer interface (BCI) system in recent years. The advantages of SSVEP-BCI system include high classification accuracy, fast information transform rate and strong anti-interference ability. Most of the traditional researches induce SSVEP responses in low and middle frequency bands as control signals. However, SSVEP in this frequency band may cause visual fatigue and even induce epilepsy in subjects. In contrast, high-frequency SSVEP-BCI provides a more comfortable and natural interaction despite its lower amplitude and weaker response. Therefore, it has been widely concerned by researchers in recent years. This paper summarized and analyzed the related research of high-frequency SSVEP-BCI in the past ten years from the aspects of paradigm and algorithm. Finally, the application prospect and development direction of high-frequency SSVEP were discussed and prospected.

RevDate: 2023-02-28

Dadarlat MC, Canfield RA, AL Orsborn (2022)

Neural Plasticity in Sensorimotor Brain-Machine Interfaces.

Annual review of biomedical engineering [Epub ahead of print].

Brain-machine interfaces (BMIs) aim to treat sensorimotor neurological disorders by creating artificial motor and/or sensory pathways. Introducing artificial pathways creates new relationships between sensory input and motor output, which the brain must learn to gain dexterous control. This review highlights the role of learning in BMIs to restore movement and sensation, and discusses how BMI design may influence neural plasticity and performance. The close integration of plasticity in sensory and motor function influences the design of both artificial pathways and will be an essential consideration for bidirectional devices that restore both sensory and motor function. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 25 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

RevDate: 2023-02-28

Zhang Y, Qiu S, H He (2023)

Multimodal motor imagery decoding method based on temporal spatial feature alignment and fusion.

Journal of neural engineering [Epub ahead of print].

A motor imagery-based brain-computer interface (MI-BCI) translates spontaneous movement intention from the brain to outside devices. MI-BCI systems based on a single modality have been widely researched in recent decades. Lately, along with the development of neuroimaging methods, multimodal MI-BCI studies that use multiple neural signals have been proposed, which are promising for enhancing the decoding accuracy of MI-BCI. Multimodal MI data contain rich common and complementary information. Effective feature representations are helpful to promote the performance of classification tasks. Thus, it is very important to explore and extract features with higher separability and robustness from the rich information in multimodal data. Approach: In this study, a five-class motor imagery experiment was designed. Electroencephalography and functional near infrared spectroscopy data were collected simultaneously. A multimodal MI decoding neural network was proposed. In this network, to enhance the feature representation, the heterogeneous data of different modalities in the spatial dimension were aligned through the proposed spatial alignment losses. Also, the multimodal features were aligned and fused in the temporal dimension by an attention-based modality fusion module. Main results and Significance: The collected dataset was analyzed from temporal, spatial and frequency perspectives. The results showed that the multimodal data contain visually separable motor imagery patterns. The experimental results show that the proposed decoding method achieved the highest decoding accuracy among the compared methods on the self-collected dataset and a public dataset. Ablation results show that each part of the proposed method is effective. Compared with single-modality decoding, the proposed method obtained 4.6% higher decoding accuracy on the self-collected dataset. This indicates that the proposed method can improve the performance of multimodal MI decoding. This study provides a new approach for capturing the rich information in multimodal MI data and enhancing multimodal MI-BCI decoding accuracy. .

RevDate: 2023-02-28

Amiri M, Nazari S, Jafari AH, et al (2023)

A new full closed-loop brain-machine interface approach based on neural activity: A study based on modeling and experimental studies.

Heliyon, 9(3):e13766.

BACKGROUND: The bidirectional brain-machine interfaces algorithms are machines that decode neural response in order to control the external device and encode position of artificial limb to proper electrical stimulation, so that the interface between brain and machine closes. Most BMI researchers typically consider four basic elements: recording technology to extract brain activity, decoding algorithm to translate brain activity to the predicted movement of the external device, external device (prosthetic limb such as a robotic arm), and encoding interface to convert the motion of the external machine to set of the electrical stimulation of the brain.

NEW METHOD: In this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for sensory cortex (S1) connected to the neural network model of motor cortex (M1) considering the topographic mapping between S1 and M1. We use 4-box model in S1 and 4-box in M1 so that each box contains 500 neurons. Individual boxes include inhibitory and excitatory neurons and synapses. Next, we develop a new BMI algorithm based on neural activity. The main concept of this BMI algorithm is to close the loop between brain and mechaical external device.

RESULTS: The sensory interface as encoding algorithm convert the location of the external device (artificial limb) into the electrical stimulation which excite the S1 model. The motor interface as decoding algorithm convert neural recordings from the M1 model into a force which causes the movement of the external device. We present the simulation results for the on line BMI which means that there is a real time information exchange between 9 boxes and 4 boxes of S1-M1 network model and the external device. Also, off line information exchange between brain of five anesthetized rats and externnal device was performed. The proposed BMI algorithm has succeeded in controlling the movement of the mechanical arm towards the target area on simulation and experimental data, so that the BMI algorithm shows acceptable WTPE and the average number of iterations of the algorithm in reaching artificial limb to the target region.Comparison with existing methods and Conclusions: In order to confirm the simulation results the 9-box model of S1-M1 network was developed and the valid "spike train" algorithm, which has good results on real data, is used to compare the performance accuracy of the proposed BMI algorithm versus "spike train" algorithm on simulation and off line experimental data of anesthetized rats. Quantitative and qualitative results confirm the proper performance of the proposed algorithm compared to algorithm "spike train" on simulations and experimental data.

RevDate: 2023-02-28

Lin CL, LT Chen (2023)

Improvement of brain-computer interface in motor imagery training through the designing of a dynamic experiment and FBCSP.

Heliyon, 9(3):e13745.

Motor imagery (MI) can produce a specific brain pattern when the subject imagines performing a particular action without any actual body movements. According to related previous research, the improvement of the training of MI brainwaves can be adopted by feedback methods in which the analysis of brainwave characteristics is very important. The aim of this study was to improve the subject's MI and the accuracy of classification. In order to ameliorate the accuracy of the MI of the left and right hand, the present study designed static and dynamic visual stimuli in experiments so as to evaluate which one can improve subjects' imagination training. Additionally, the filter bank common spatial pattern (FBCSP) method was used to divide the frequency band range of the brainwaves into multiple segments, following which linear discriminant analysis (LDA) was adopted for classification. The results revealed that the averaged false positive rate (FPR) under FBCSP-LDA in the dynamic MI experiment was the lowest FPR (23.76%). As such, this study suggested that a combination of the dynamic MI experiment and the FBCSP-LDA method improved the overall prediction error rate and ameliorated the performance of the MI brain-computer interface.

RevDate: 2023-02-28

Ghodousi M, Pousson JE, Bernhofs V, et al (2023)

Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application.

Sensors (Basel, Switzerland), 23(4):.

A Brain-Computer Music Interface (BCMI) system may be designed to harness electroencephalography (EEG) signals for control over musical outputs in the context of emotionally expressive performance. To develop a real-time BCMI system, accurate and computationally efficient emotional biomarkers should first be identified. In the current study, we evaluated the ability of various features to discriminate between emotions expressed during music performance with the aim of developing a BCMI system. EEG data was recorded while subjects performed simple piano music with contrasting emotional cues and rated their success in communicating the intended emotion. Power spectra and connectivity features (Magnitude Square Coherence (MSC) and Granger Causality (GC)) were extracted from the signals. Two different approaches of feature selection were used to assess the contribution of neutral baselines in detection accuracies; 1- utilizing the baselines to normalize the features, 2- not taking them into account (non-normalized features). Finally, the Support Vector Machine (SVM) has been used to evaluate and compare the capability of various features for emotion detection. Best detection accuracies were obtained from the non-normalized MSC-based features equal to 85.57 ± 2.34, 84.93 ± 1.67, and 87.16 ± 0.55 for arousal, valence, and emotional conditions respectively, while the power-based features had the lowest accuracies. Both connectivity features show acceptable accuracy while requiring short processing time and thus are potential candidates for the development of a real-time BCMI system.

RevDate: 2023-02-28

Siribunyaphat N, Y Punsawad (2023)

Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control.

Sensors (Basel, Switzerland), 23(4):.

Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.

RevDate: 2023-02-28

Xie Y, S Oniga (2023)

Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.

Sensors (Basel, Switzerland), 23(4):.

In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time-frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.

RevDate: 2023-02-27

Letner JG, Patel PR, Hsieh JC, et al (2023)

Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship.

APPROACH: Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8 µm diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12 weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50 µm radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates.

MAIN RESULTS: Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7±4.6 µm, X̄±S) in layer V motor cortex.

SIGNIFICANCE: Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.

RevDate: 2023-02-27

Gupta A, Daniel R, Rao A, et al (2023)

Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.

Big data [Epub ahead of print].

With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.

RevDate: 2023-02-27

Yang Y, Garringer HJ, Shi Y, et al (2023)

New SNCA mutation and structures of α-synuclein filaments from juvenile-onset synucleinopathy.

Acta neuropathologica [Epub ahead of print].

A 21-nucleotide duplication in one allele of SNCA was identified in a previously described disease with abundant α-synuclein inclusions that we now call juvenile-onset synucleinopathy (JOS). This mutation translates into the insertion of MAAAEKT after residue 22 of α-synuclein, resulting in a protein of 147 amino acids. Both wild-type and mutant proteins were present in sarkosyl-insoluble material that was extracted from frontal cortex of the individual with JOS and examined by electron cryo-microscopy. The structures of JOS filaments, comprising either a single protofilament, or a pair of protofilaments, revealed a new α-synuclein fold that differs from the folds of Lewy body diseases and multiple system atrophy (MSA). The JOS fold consists of a compact core, the sequence of which (residues 36-100 of wild-type α-synuclein) is unaffected by the mutation, and two disconnected density islands (A and B) of mixed sequences. There is a non-proteinaceous cofactor bound between the core and island A. The JOS fold resembles the common substructure of MSA Type I and Type II dimeric filaments, with its core segment approximating the C-terminal body of MSA protofilaments B and its islands mimicking the N-terminal arm of MSA protofilaments A. The partial similarity of JOS and MSA folds extends to the locations of their cofactor-binding sites. In vitro assembly of recombinant wild-type α-synuclein, its insertion mutant and their mixture yielded structures that were distinct from those of JOS filaments. Our findings provide insight into a possible mechanism of JOS fibrillation in which mutant α-synuclein of 147 amino acids forms a nucleus with the JOS fold, around which wild-type and mutant proteins assemble during elongation.

RevDate: 2023-02-27

Chen D, Liu K, Guo J, et al (2023)

Editorial: Brain-computer interface and its applications.

Frontiers in neurorobotics, 17:1140508.

RevDate: 2023-02-27

Chen PW, Ji DH, Zhang YS, et al (2023)

Electroactive and Stretchable Hydrogels of 3,4-Ethylenedioxythiophene/thiophene Copolymers.

ACS omega, 8(7):6753-6761.

Hydrogels are conductive and stretchable, allowing for their use in flexible electronic devices, such as electronic skins, sensors, human motion monitoring, brain-computer interface, and so on. Herein, we synthesized the copolymers having various molar ratios of 3,4-ethylenedioxythiophene (EDOT) to thiophene (Th), which served as conductive additives. With doping engineering and incorporation with P(EDOT-co-Th) copolymers, hydrogels have presented excellent physical/chemical/electrical properties. It was found that the mechanical strength, adhesion ability, and conductivity of hydrogels were highly dependent on the molar ratio of EDOT to Th of the copolymers. The more the EDOT, the stronger the tensile strength and the greater the conductivity, but the lower the elongation break tends to be. By comprehensively evaluating the physical/chemical/electrical properties and cost of material use, the hydrogel incorporated with a 7:3 molar ratio P(EDOT-co-Th) copolymer was an optimal formulation for soft electronic devices.

RevDate: 2023-02-27

Song M, Huang Y, Shen Y, et al (2022)

A 1.66Gb/s and 5.8pJ/b Transcutaneous IR-UWB Telemetry System with Hybrid Impulse Modulation for Intracortical Brain-Computer Interfaces.

Digest of technical papers. IEEE International Solid-State Circuits Conference, 2022:394-396.

RevDate: 2023-02-27

Branco MP, Geukes SH, Aarnoutse EJ, et al (2023)

Nine decades of electrocorticography: a comparison between epidural and subdural recordings.

The European journal of neuroscience [Epub ahead of print].

In recent years, electrocorticography (ECoG) has arisen as a neural signal recording tool in the development of clinically viable neural interfaces. ECoG electrodes are generally placed below the dura mater (subdural) but can also be placed on top of the dura (epidural). In deciding which of these modalities best suits long-term implants, complications and signal quality are important considerations. Conceptually, epidural placement may present a lower risk of complications as the dura is left intact, but also a lower signal quality due to the dura acting as a signal attenuator. The extent to which complications and signal quality are affected by the dura, however, has been a matter of debate. To improve our understanding of the effects of the dura on complications and signal quality, we conducted a literature review. We inventorized the effect of the dura on signal quality, decodability and longevity of acute and chronic ECoG recordings in humans and non-human primates. Also, we compared the incidence and nature of serious complications in studies that employed epidural and subdural ECoG. Overall, we found that, even though epidural recordings exhibit attenuated signal amplitude over subdural recordings, particularly for high-density grids, the decodability of epidural recorded signals does not seem to be markedly affected. Additionally, we found that the nature of serious complications was comparable between epidural and subdural recordings. These results indicate that both epidural and subdural ECoG may be suited for long-term neural signal recordings, at least for current generations of clinical and high-density ECoG grids.

RevDate: 2023-02-26

Hua SS, Ding JJ, Sun TC, et al (2023)

NMDAR-dependent synaptic potentiation via APPL1 signaling is required for the accessibility of a prefrontal neuronal assembly in retrieving fear extinction.

Biological psychiatry pii:S0006-3223(23)00087-2 [Epub ahead of print].

BACKGROUND: The ventromedial prefrontal cortex (vmPFC) has been viewed as a locus to store and recall extinction memory. However, the synaptic and cellular mechanisms underlying this process remain elusive.

METHODS: We combined transgenic mice, electrophysiological recording, activity-dependent cell labeling, and chemogenetic manipulation to analyze the role of adaptor protein APPL1 in the vmPFC for fear extinction retrieval.

RESULTS: We found that both constitutive and conditional APPL1 knockout decreases NMDA receptor (NMDAR) function in the vmPFC and impairs fear extinction retrieval. Moreover, APPL1 undergoes nuclear translocation during extinction retrieval. Blocking APPL1 nucleocytoplasmic translocation reduces NMDAR currents and disrupts extinction retrieval. We further identified a prefrontal neuronal ensemble that is both necessary and sufficient for the storage of extinction memory. Inducible APPL1 knockout in this ensemble abolishes NMDAR-dependent synaptic potentiation and disrupts extinction retrieval, while simultaneously chemogenetic activation of this ensemble rescues the impaired behaviors.

CONCLUSIONS: Therefore, our results indicate that a prefrontal neuronal ensemble stores extinction memory, and APPL1 signaling supports these neurons to retrieve extinction memory via controlling NMDAR-dependent potentiation.

RevDate: 2023-02-26

Liu Z, Wang L, Xu S, et al (2022)

A multiwavelet-based sparse time-varying autoregressive modeling for motor imagery EEG classification.

Computers in biology and medicine, 155:106196 pii:S0010-4825(22)00904-0 [Epub ahead of print].

Brain-computer Interface (BCI) system based on motor imagery (MI) heavily relies on electroencephalography (EEG) recognition with high accuracy. However, modeling and classification of MI EEG signals remains a challenging task due to the non-linear and non-stationary characteristics of the signals. In this paper, a new time-varying modeling framework combining multiwavelet basis functions and regularized orthogonal forward regression (ROFR) algorithm is proposed for the characterization and classification of MI EEG signals. Firstly, the time-varying coefficients of the time-varying autoregressive (TVAR) model are precisely approximated with the multiwavelet basis functions. Then a powerful ROFR algorithm is employed to dramatically alleviate the redundant model structure and accurately recover the relevant time-varying model parameters to obtain high resolution power spectral density (PSD) features. Finally, the features are sent to different classifiers for the classification task. To effectively improve the accuracy of classification, a principal component analysis (PCA) algorithm is utilized to determine the best feature subset and Bayesian optimization algorithm is performed to obtain the optimal parameters of the classifier. The proposed method achieves satisfactory classification accuracy on the public BCI Competition II Dataset III, which proves that this method potentially improves the recognition accuracy of MI EEG signals, and has great significance for the construction of BCI system based on MI.

RevDate: 2023-02-25

De Rubis G, Paudel KR, Manandhar B, et al (2023)

Agarwood Oil Nanoemulsion Attenuates Cigarette Smoke-Induced Inflammation and Oxidative Stress Markers in BCi-NS1.1 Airway Epithelial Cells.

Nutrients, 15(4):.

Chronic obstructive pulmonary disease (COPD) is an irreversible inflammatory respiratory disease characterized by frequent exacerbations and symptoms such as cough and wheezing that lead to irreversible airway damage and hyperresponsiveness. The primary risk factor for COPD is chronic cigarette smoke exposure, which promotes oxidative stress and a general pro-inflammatory condition by stimulating pro-oxidant and pro-inflammatory pathways and, simultaneously, inactivating anti-inflammatory and antioxidant detoxification pathways. These events cause progressive damage resulting in impaired cell function and disease progression. Treatments available for COPD are generally aimed at reducing the symptoms of exacerbation. Failure to regulate oxidative stress and inflammation results in lung damage. In the quest for innovative treatment strategies, phytochemicals, and complex plant extracts such as agarwood essential oil are promising sources of molecules with antioxidant and anti-inflammatory activity. However, their clinical use is limited by issues such as low solubility and poor pharmacokinetic properties. These can be overcome by encapsulating the therapeutic molecules using advanced drug delivery systems such as polymeric nanosystems and nanoemulsions. In this study, agarwood oil nanoemulsion (agarwood-NE) was formulated and tested for its antioxidant and anti-inflammatory potential in cigarette smoke extract (CSE)-treated BCi-NS1.1 airway basal epithelial cells. The findings suggest successful counteractivity of agarwood-NE against CSE-mediated pro-inflammatory effects by reducing the expression of the pro-inflammatory cytokines IL-1α, IL-1β, IL-8, and GDF-15. In addition, agarwood-NE induced the expression of the anti-inflammatory mediators IL-10, IL-18BP, TFF3, GH, VDBP, relaxin-2, IFN-γ, and PDGF. Furthermore, agarwood-NE also induced the expression of antioxidant genes such as GCLC and GSTP1, simultaneously activating the PI3K pro-survival signalling pathway. This study provides proof of the dual anti-inflammatory and antioxidant activity of agarwood-NE, highlighting its enormous potential for COPD treatment.

RevDate: 2023-02-26

Zhang Y, Zou J, N Ding (2023)

Acoustic correlates of the syllabic rhythm of speech: Modulation spectrum or local features of the temporal envelope.

Neuroscience and biobehavioral reviews, 147:105111 pii:S0149-7634(23)00080-5 [Epub ahead of print].

The syllable is a perceptually salient unit in speech. Since both the syllable and its acoustic correlate, i.e., the speech envelope, have a preferred range of rhythmicity between 4 and 8 Hz, it is hypothesized that theta-band neural oscillations play a major role in extracting syllables based on the envelope. A literature survey, however, reveals inconsistent evidence about the relationship between speech envelope and syllables, and the current study revisits this question by analyzing large speech corpora. It is shown that the center frequency of speech envelope, characterized by the modulation spectrum, reliably correlates with the rate of syllables only when the analysis is pooled over minutes of speech recordings. In contrast, in the time domain, a component of the speech envelope is reliably phase-locked to syllable onsets. Based on a speaker-independent model, the timing of syllable onsets explains about 24% variance of the speech envelope. These results indicate that local features in the speech envelope, instead of the modulation spectrum, are a more reliable acoustic correlate of syllables.

RevDate: 2023-02-25

Lin A, Wang T, Li C, et al (2023)

Association of Sarcopenia with Cognitive Function and Dementia Risk Score: A National Prospective Cohort Study.

Metabolites, 13(2): pii:metabo13020245.

The relationship between skeletal muscle and cognitive disorders has drawn increasing attention. This study aims to examine the associations of sarcopenia with cognitive function and dementia risk score. Data on 1978 participants (aged 65 years and older) from the 2011 wave of the China Health and Retirement Longitudinal Study, with four follow-up waves to 2018, were used. Cognitive function was assessed by four dimensions, with a lower score indicating lower cognitive function. Dementia risk was assessed by a risk score using the Rotterdam Study Basic Dementia Risk Model (BDRM), with a higher score indicating a greater risk. Sarcopenia was defined when low muscle mass plus low muscle strength or low physical performance were met. We used generalized estimating equations to examine the associations of sarcopenia. In the fully adjusted models, sarcopenia was significantly associated with lower cognitive function (standardized, β = -0.15; 95% CIs: -0.26, -0.04) and a higher BDRM score (standardized, β = 0.42; 95% CIs: 0.29, 0.55). Our findings may provide a new avenue for alleviating the burden of cognitive disorders by preventing sarcopenia.


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

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

Research Gate page for R J Robbins

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

Curriculum Vitae for R J Robbins

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