المؤلفون: E. Huang, Y. Fang, Xufei Zheng, Z. Zhang

المصدر: IRBM. 43:107-113

مصطلحات موضوعية: Computer science, business.industry, Interface (computing), 0206 medical engineering, Biomedical Engineering, Biophysics, Pattern recognition, 02 engineering and technology, 020601 biomedical engineering, Convolutional neural network, 030218 nuclear medicine & medical imaging, Weighting, Domain (software engineering), 03 medical and health sciences, 0302 clinical medicine, Motor imagery, Frequency domain, Time domain, Artificial intelligence, business, Brain–computer interface

الوصف: Background and objective An important task of the brain-computer interface (BCI) of motor imagery is to extract effective time-domain features, frequency-domain features or time-frequency domain features from the raw electroencephalogram (EEG) signals for classification of motor imagery. However, choosing an appropriate method to combine time domain and frequency domain features to improve the performance of motor imagery recognition is still a research hotspot. Methods In order to fully extract and utilize the time-domain and frequency-domain features of EEG in classification tasks, this paper proposed a novel dual-stream convolutional neural network (DCNN), which can use time domain signal and frequency domain signal as the inputs, and the extracted time-domain features and frequency-domain features are fused by linear weighting for classification training. Furthermore, the weight can be learned by the DCNN automatically. Results The experiments based on BCI competition II dataset III and BCI competition IV dataset 2a showed that the model proposed by this study has better performance than other conventional methods. The model used time-frequency signal as the inputs had better performance than the model only used time-domain signals or frequency-domain signals. The accuracy of classification was improved for each subject compared with the models only used one signals as the inputs. Conclusions Further analysis shown that the fusion weight of different subject is specifically, adjusting the weight coefficient automatically is helpful to improve the classification accuracy.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::e18ad76126186034527379d2c38d4a6eTest
https://doi.org/10.1016/j.irbm.2021.04.004Test

المؤلفون: Mariana Gongora, Guaraci Ken Tanaka, Henning Budde, Juliana Bittencourt, Danielle Aprigio, Marco Orsini, Bruna Velasques, Silmar Teixeira, Pedro Ribeiro, Mauricio Cagy

المساهمون: Associacao Arquivos De Neuro-Psiquitria Dr Oswaldo Lange

المصدر: Arquivos de Neuro-Psiquiatria, Vol 78, Iss 4, Pp 199-205 (2020)
Arquivos de Neuro-Psiquiatria, Volume: 78, Issue: 4, Pages: 199-205, Published: 09 APR 2020
Arquivos de Neuro-Psiquiatria v.78 n.4 2020
Arquivos de neuro-psiquiatria
Academia Brasileira de Neurologia
instacron:ABNEURO
Arquivos de Neuro-Psiquiatria, Issue: ahead, Published: 09 APR 2020

مصطلحات موضوعية: Adult, Imagery, Psychotherapy, Movement, Dopamine Agents, 0206 medical engineering, methylphenidate, Neurosciences. Biological psychiatry. Neuropsychiatry, 02 engineering and technology, Electroencephalography, beta rhythm, 03 medical and health sciences, ritmo beta, 0302 clinical medicine, Motor imagery, motor imagery, medicine, Humans, Beta Rhythm, risperidona, risperidone, medicine.diagnostic_test, Methylphenidate, Dopaminergic, eletroencefalografia, Cognition, imagética motora, 020601 biomedical engineering, medicine.anatomical_structure, Neurology, Dopaminergic pathways, Neurology (clinical), Analysis of variance, Psychology, Neuroscience, 030217 neurology & neurosurgery, metilfenidato, electroencephalography, medicine.drug, RC321-571

الوصف: BACKGROUND: Motor Imagery (MI) represents the cognitive component of the movement and recruits dopaminergic systems. OBJECTIVE: To investigate the role of dopaminergic system through the action of methylphenidate and risperidone over beta coherence during execution, action observation and motor imagery. METHODS: Electroencephalography (EEG) data were recorded before and after the substance intake. For statistical analysis, a three-way ANOVA was used to identify changes in beta coherence induced by the group, task and the moment variables. Statistical significance was set at p≤0.007. RESULTS: We found a main effect for group for C3/CZ, and a main effect for task for CZ/C4 pairs of electrodes. Furthermore, significant differences were found in the post-drug administration between groups for C3/CZ pair of electrodes, and between task for C4/CZ pair of electrodes. CONCLUSION: The administration of methylphenidate and risperidone was able to produce electrocortical changes of the cortical central regions, even when featuring antagonistic effects on the dopaminergic pathways. Moreover, the execution task allowed beta-band modulation increase.
Introdução: A imagética motora (IM) representa o componente cognitivo do movimento e recruta os sistemas dopaminérgicos. Objetivo: Investigar o papel do sistema dopaminérgico por meio da ação do metilfenidato e da risperidona sobre a coerência em beta durante a execução, observação de ação e imagética motora. Métodos: Os dados de eletroencefalografia (EEG) foram registrados antes e depois da ingestão das substâncias. Para a análise estatística, uma ANOVA de três vias foi utilizada para identificar mudanças na coerência beta induzidas pelas variáveis grupo, tarefa e momento. A significância estatística foi estabelecida em p≤0,007. Resultados: Encontramos um efeito principal para o grupo C3/CZ e um efeito principal para a tarefa nos pares de eletrodos CZ/C4. Além disso, diferenças significativas foram encontradas após a administração da droga entre os grupos para o par de eletrodos C3/CZ e entre tarefa para o par de eletrodos C4/CZ. Conclusão: A administração de metilfenidato e risperidona foi capaz de produzir alterações eletrocorticais das regiões somatomotoras, mesmo apresentando efeitos antagônicos nas vias dopaminérgicas. Além disso, a tarefa de execução provocou maior modulação da banda beta.

وصف الملف: application/pdf; text/html

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e79a43783959d9e288aab3178566d9d7Test
http://www.scielo.br/pdf/anp/v78n4/1678-4227-anp-0004-282x20190186.pdfTest

المؤلفون: Fang Wang, Yimin Hou, Xiangmin Lun, Zhenglin Yu, Tao Chen

المصدر: Journal of Intelligent & Fuzzy Systems. 40:5275-5288

مصطلحات موضوعية: Statistics and Probability, Computer science, business.industry, 0206 medical engineering, General Engineering, 02 engineering and technology, 020601 biomedical engineering, Motor imagery, Artificial Intelligence, Electrode, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer vision, Artificial intelligence, business

الوصف: In order to develop an efficient brain-computer interface system, the brain activity measured by electroencephalography needs to be accurately decoded. In this paper, a motor imagery classification approach is proposed, combining virtual electrodes on the cortex layer with a convolutional neural network; this can effectively improve the decoding performance of the brain-computer interface system. A three layer (cortex, skull, and scalp) head volume conduction model was established by using the symmetric boundary element method to map the scalp signal to the cortex area. Nine pairs of virtual electrodes were created on the cortex layer, and the features of the time and frequency sequence from the virtual electrodes were extracted by performing time-frequency analysis. Finally, the convolutional neural network was used to classify motor imagery tasks. The results show that the proposed approach is convergent in both the training model and the test model. Based on the Physionet motor imagery database, the averaged accuracy can reach 98.32% for a single subject, while the averaged values of accuracy, Kappa, precision, recall, and F1-score on the group-wise are 96.23%, 94.83%, 96.21%, 96.13%, and 96.14%, respectively. Based on the High Gamma database, the averaged accuracy has achieved 96.37% and 91.21% at the subject and group levels, respectively. Moreover, this approach is superior to those of other studies on the same database, which suggests robustness and adaptability to individual variability.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::735ccf77537e082f52933de42656fc1fTest
https://doi.org/10.3233/jifs-202046Test

المؤلفون: Shin-ichi Izumi, David Achanccaray, Mitsuhiro Hayashibe

المصدر: Computational Intelligence and Neuroscience, Vol 2021 (2021)

مصطلحات موضوعية: medicine.medical_specialty, Article Subject, General Computer Science, General Mathematics, medicine.medical_treatment, media_common.quotation_subject, Computer applications to medicine. Medical informatics, 0206 medical engineering, R858-859.7, Neurosciences. Biological psychiatry. Neuropsychiatry, 02 engineering and technology, Virtual reality, Premotor cortex, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Motor imagery, Perception, medicine, Stroke, media_common, Brain–computer interface, Rehabilitation, Proprioception, General Neuroscience, General Medicine, medicine.disease, 020601 biomedical engineering, medicine.anatomical_structure, Psychology, 030217 neurology & neurosurgery, RC321-571

الوصف: In the aging society, the number of people suffering from vascular disorders is rapidly increasing and has become a social problem. The death rate due to stroke, which is the second leading cause of global mortality, has increased by 40% in the last two decades. Stroke can also cause paralysis. Of late, brain-computer interfaces (BCIs) have been garnering attention in the rehabilitation field as assistive technology. A BCI for the motor rehabilitation of patients with paralysis promotes neural plasticity, when subjects perform motor imagery (MI). Feedback, such as visual and proprioceptive, influences brain rhythm modulation to contribute to MI learning and motor function restoration. Also, virtual reality (VR) can provide powerful graphical options to enhance feedback visualization. This work aimed to improve immersive VR-BCI based on hand MI, using visual-electrotactile stimulation feedback instead of visual feedback. The MI tasks include grasping, flexion/extension, and their random combination. Moreover, the subjects answered a system perception questionnaire after the experiments. The proposed system was evaluated with twenty able-bodied subjects. Visual-electrotactile feedback improved the mean classification accuracy for the grasping (93.00% ± 3.50%) and flexion/extension (95.00% ± 5.27%) MI tasks. Additionally, the subjects achieved an acceptable mean classification accuracy (maximum of 86.5% ± 5.80%) for the random MI task, which required more concentration. The proprioceptive feedback maintained lower mean power spectral density in all channels and higher attention levels than those of visual feedback during the test trials for the grasping and flexion/extension MI tasks. Also, this feedback generated greater relative power in the μ -band for the premotor cortex, which indicated better MI preparation. Thus, electrotactile stimulation along with visual feedback enhanced the immersive VR-BCI classification accuracy by 5.5% and 4.5% for the grasping and flexion/extension MI tasks, respectively, retained the subject’s attention, and eased MI better than visual feedback alone.

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الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3cfc72fbf58d49a075d1d1189145dcf3Test
https://doi.org/10.1155/2021/8832686Test

المؤلفون: Zhenghui Gu, Zhu Liang Yu, Yuanqing Li, Tianyou Yu, Zhenfu Wen, Wei Wu, Feifei Qi

المصدر: IEEE Transactions on Cybernetics. 51:558-567

مصطلحات موضوعية: Optimization problem, Computer science, 0206 medical engineering, Feature extraction, 02 engineering and technology, Electroencephalography, Motor imagery, Discriminative model, 0202 electrical engineering, electronic engineering, information engineering, medicine, Electrical and Electronic Engineering, Selection (genetic algorithm), medicine.diagnostic_test, business.industry, Pattern recognition, Filter (signal processing), 020601 biomedical engineering, Computer Science Applications, Human-Computer Interaction, Control and Systems Engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, Software, Information Systems, Communication channel

الوصف: Achieving high classification performance in electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often entails a large number of channels, which impedes their use in practical applications. Despite the previous efforts, it remains a challenge to determine the optimal subset of channels in a subject-specific manner without heavily compromising the classification performance. In this article, we propose a new method, called spatiotemporal-filtering-based channel selection (STECS), to automatically identify a designated number of discriminative channels by leveraging the spatiotemporal information of the EEG data. In STECS, the channel selection problem is cast under the framework of spatiotemporal filter optimization by incorporating a group sparsity constraints, and a computationally efficient algorithm is developed to solve the optimization problem. The performance of STECS is assessed on three motor imagery EEG datasets. Compared with state-of-the-art spatiotemporal filtering algorithms using full EEG channels, STECS yields comparable classification performance with only half of the channels. Moreover, STECS significantly outperforms the existing channel selection methods. These results suggest that this algorithm holds promise for simplifying BCI setups and facilitating practical utility.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e546fb73326c4ff18663ad09d579630Test
https://doi.org/10.1109/tcyb.2019.2963709Test

المؤلفون: K. Renuga Devi, H. Hannah Inbarani

المصدر: Journal on Multimodal User Interfaces. 15:301-321

مصطلحات موضوعية: medicine.diagnostic_test, Discretization, Computer science, business.industry, 0206 medical engineering, SIGNAL (programming language), Pattern recognition, 02 engineering and technology, Function (mathematics), Electroencephalography, 020601 biomedical engineering, Field (computer science), Human-Computer Interaction, Motor imagery, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, Rough set, Artificial intelligence, business, Brain–computer interface

الوصف: Brain Computer Interface is an interesting and important research field that has contributed widespread application systems. In the medical field, it is important for physically challenged persons to aid in rehabilitation and restoration. In Brain Computer Interface, computer acts as interface between brain signals and external device. The computer processes the brain signals and sends necessary instructions to external device. The external device helps in restoring the movement ability of patient. Motor imagery is the imagination of motor movements like hand, foot and tongue. There is an associated brain signal when the normal person moves their hand, foot and tongue. Similarly, there is an associated brain signal when the physically challenged person imagines moving their hand, foot and tongue. When this brain signal is analyzed by brain computer interface, it can facilitate motor movements through external device. The aim of this work is to analyze and classify the brain signals for motor movements to aid in rehabilitation and restoration. In this paper BCI Competition IV Dataset I, Dataset IIa, BCI Competition III Dataset IIIa and Neuroprosthetic EEG Dataset are analyzed A novel optimization technique with Neighborhood Decision Theoretic Rough Set under Dynamic Granulation is proposed for motor imagery classification. Neighborhood based Decision Theoretic Rough Set under Dynamic Granulation (NDTRS under DG) is hybrid approach combining two algorithms Neighborhood Rough Set and Decision Theoretic Rough Set under Dynamic Granulation ((DTRS under DG). Neighborhood Rough Set overcomes the drawback of discretization step in Rough Set. Decision Theoretic Rough Set under Dynamic Granulation algorithm has loss function for classification. The effectiveness of classification is improved since the loss function is involved in the construction of algorithm. The proposed method Neighborhood based Decision Theoretic Rough Set under Dynamic Granulation gives higher classification accuracy compared to existing approaches.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::115fd638cafb2cc05d457bb200869f5aTest
https://doi.org/10.1007/s12193-020-00358-4Test

المؤلفون: Chenguang Li, Hongjun Yang, Long Cheng

المصدر: Complex & Intelligent Systems. 8:731-741

مصطلحات موضوعية: Computer science, business.industry, Interface (computing), 0206 medical engineering, Pattern recognition, 02 engineering and technology, General Medicine, 020601 biomedical engineering, Signal, Hilbert–Huang transform, Task (project management), Random forest, Support vector machine, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, Artificial intelligence, business, 030217 neurology & neurosurgery, Brain–computer interface

الوصف: As a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::d3c489afa0f07f02a5e158d4b9ac75d9Test
https://doi.org/10.1007/s40747-020-00266-wTest

المؤلفون: Daniel Prado Campos, Marcelo C. M. Teixeira, André Eugênio Lazzaretti, Eddy Krueger, Paulo Broniera Junior, Aparecido Augusto de Carvalho, Percy Nohama

المساهمون: Universidade Estadual Paulista (Unesp), Universidade Tecnológica Federal do Paraná (UTFPR), Universidade Estadual de Londrina (UEL), IoT e Manufatura 4.0

المصدر: Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP

مصطلحات موضوعية: medicine.medical_specialty, Computer science, 0206 medical engineering, Biomedical Engineering, Closed-loop systems, Health Informatics, 02 engineering and technology, Isometric exercise, Spinal cord injury, Electroencephalography, Setpoint, 03 medical and health sciences, 0302 clinical medicine, Physical medicine and rehabilitation, Motor imagery, Functional electrical stimulation, medicine, Brain–computer interface, medicine.diagnostic_test, medicine.disease, 020601 biomedical engineering, Neuromodulation (medicine), Signal Processing, Motor Imagery, Paraplegia, Mechanomyography, 030217 neurology & neurosurgery

الوصف: Made available in DSpace on 2021-06-25T10:29:02Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-07-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) People with spinal cord injury (SCI) may have their paralyzed muscles activated through functional electrical stimulation (FES). This neuromodulation technique has been used frequently to assist in controlling the movement of neuroprostheses. Electroencephalography (EEG) is able to trigger FES from the motor imagery captured through movements intentions. This research presents an isometric neuromuscular control system of the quadriceps muscle activated by EEG. Additionally, the detection of neuromuscular fatigue through the mechanomyography (MMG) technique is proposed, which is used to shut-off the system. A pilot study was performed on a chronic 42-year-old paraplegic (no voluntary contraction below the spinal cord injury level T8) volunteer. To do so, the training procedure for EEG signals was divided into the calibration and feedback phases. In the first one, four EEG channels and the Linear Discriminant Analysis (LDA) classifier were used to classify between motor imagery of the right leg and remain at rest. The maximum accuracy obtained during this stage was 77%. In the feedback phase, the volunteer was able to activate FES through brain–computer interface (BCI) in two tests (defined as Test 1 and Test 2) with the same procedure in different days. The closed-loop force control was tested with the setpoint of 2 kgf and 2.5 kgf and proved to be stable on both tests, successfully turning off the FES using the fatigue threshold from the MMG signal, being the main contribution of this work. Universidade Estadual Paulista Júlio Mesquita Filho (UNESP) Faculdade de Engenharia Campus Ilha Solteira, Av. Brasil Sul, 56 Universidade Tecnológica Federal do Paraná (UTFPR), Marcílio Dias, 635 Universidade Tecnológica Federal do Paraná (UTFPR), Avenida Sete de Setembro 3165 Universidade Estadual de Londrina (UEL) – Departamento de Anatomia Laboratório de Engenharia Neural e de Reabilitação, Rodovia Celso Garcia Cid – Pr 445, Km 380 Instituto Senai de Tecnologia da Informação e Comunicação (ISTIC) Laboratório de Sistemas Eletrônicos - Embarcados e de Potência IoT e Manufatura 4.0, Rua Belém 844 Universidade Estadual Paulista Júlio Mesquita Filho (UNESP) Faculdade de Engenharia Campus Ilha Solteira, Av. Brasil Sul, 56

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70fec0f73bbb946663180cd01679c641Test

المؤلفون: Yi Gu, Lei Hua

المصدر: Frontiers in Neuroscience, Vol 15 (2021)
Frontiers in Neuroscience

مصطلحات موضوعية: Computer science, Interface (computing), 0206 medical engineering, Neurosciences. Biological psychiatry. Neuropsychiatry, 02 engineering and technology, Electroencephalography, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, motor imagery, Classifier (linguistics), Feature (machine learning), medicine, human-computer interaction model, EEG signals, Brain–computer interface, Extreme learning machine, Original Research, Feature data, medicine.diagnostic_test, business.industry, General Neuroscience, Pattern recognition, BCI data set, 020601 biomedical engineering, ELM, Artificial intelligence, business, 030217 neurology & neurosurgery, Neuroscience, RC321-571

الوصف: The brain is the central nervous system that governs human activities. However, in modern society, more and more diseases threaten the health of the brain and nerves and spinal cord, making the human brain unable to conduct normal information interaction with the outside world. The rehabilitation training of the brain-computer interface can promote the nerve repair of the sensorimotor cortex in patients with brain diseases. Therefore, the research of brain-computer interface for motor imaging is of great significance for patients with brain diseases to restore motor function. Due to the characteristics of non-stationary, nonlinear, and individual differences of EEG signals, there are still many difficulties in the analysis and classification of EEG signals at this stage. In this study, the Extreme Learning Machine (ELM) model was used to classify motor-imaging EEG signals, identify the user’s intention, and control external devices. Considering that single-modal features cannot represent the core information, this study uses a fusion feature that combines temporal and spatial features as the final feature data. The fusion features are input to the trained ELM classifier, and the final classification result is obtained. Two sets of BCI competition data in the BCI competition public database are used to verify the validity of the model. The experimental results show that the ELM model has achieved a classification accuracy of 0.7832 in the classification task of Data Sets IIb, which is higher than other comparison algorithms, and shows universal applicability among different subjects. In addition, the average recognition rate of this model in the Data Sets IIIa classification task reaches 0.8347, which has obvious advantages compared with the comparative classification algorithm. The classification effect is smaller than the classification effect obtained by the champion algorithm of the same project, which has certain reference value.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::855f8263d84277c57fb6a315091a6dd2Test
https://www.frontiersin.org/articles/10.3389/fnins.2021.685119/fullTest

المؤلفون: Feiyue Ren, Zhong-Ke Gao, Miaomiao Yin, Chao Ma, Xinlin Sun, Juntao Xue, Jialing Wu

المصدر: Neural Plasticity
Neural Plasticity, Vol 2020 (2020)

مصطلحات موضوعية: Databases, Factual, Article Subject, Channel (digital image), Computer science, Movement, Interface (computing), 0206 medical engineering, Neurosciences. Biological psychiatry. Neuropsychiatry, 02 engineering and technology, Electroencephalography, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Motor imagery, medicine, Humans, Brain–computer interface, Network model, medicine.diagnostic_test, business.industry, Deep learning, Brain, Pattern recognition, 020601 biomedical engineering, Neurology, Brain-Computer Interfaces, Imagination, Neural Networks, Computer, Neurology (clinical), Artificial intelligence, business, 030217 neurology & neurosurgery, Decoding methods, RC321-571, Research Article

الوصف: Motor imagery (MI) is an important part of brain-computer interface (BCI) research, which could decode the subject’s intention and help remodel the neural system of stroke patients. Therefore, accurate decoding of electroencephalography- (EEG-) based motion imagination has received a lot of attention, especially in the research of rehabilitation training. We propose a novel multifrequency brain network-based deep learning framework for motor imagery decoding. Firstly, a multifrequency brain network is constructed from the multichannel MI-related EEG signals, and each layer corresponds to a specific brain frequency band. The structure of the multifrequency brain network matches the activity profile of the brain properly, which combines the information of channel and multifrequency. The filter bank common spatial pattern (FBCSP) algorithm filters the MI-based EEG signals in the spatial domain to extract features. Further, a multilayer convolutional network model is designed to distinguish different MI tasks accurately, which allows extracting and exploiting the topology in the multifrequency brain network. We use the public BCI competition IV dataset 2a and the public BCI competition III dataset IIIa to evaluate our framework and get state-of-the-art results in the first dataset, i.e., the average accuracy is 83.83% and the value of kappa is 0.784 for the BCI competition IV dataset 2a, and the accuracy is 89.45% and the value of kappa is 0.859 for the BCI competition III dataset IIIa. All these results demonstrate that our framework can classify different MI tasks from multichannel EEG signals effectively and show great potential in the study of remodelling the neural system of stroke patients.

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الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::869b340bba2e95f5afae9ba4e878bdbbTest
https://doi.org/10.1155/2020/8863223Test