Noninvasive neuroimaging and spatial filter transform enable ultra low delay motor imagery EEG decoding.
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| Názov: | Noninvasive neuroimaging and spatial filter transform enable ultra low delay motor imagery EEG decoding. |
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| Autori: | Fang T; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China., Wang J; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China., Mu W; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China., Song Z; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China., Zhang X; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China., Zhan G; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China., Wang P; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China., Bin J; Ji Hua Laboratory, Foshan, People's Republic of China., Niu L; Ji Hua Laboratory, Foshan, People's Republic of China., Zhang L; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China.; Ji Hua Laboratory, Foshan, People's Republic of China., Kang X; Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Institute of Meta-Medical, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China.; Ji Hua Laboratory, Foshan, People's Republic of China.; Yiwu Research Institute of Fudan University, Yiwu City, People's Republic of China.; Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, People's Republic of China.; Greater Bay Area Institute of Precision Medicine, Guangzhou, People's Republic of China. |
| Zdroj: | Journal of neural engineering [J Neural Eng] 2022 Dec 16; Vol. 19 (6). Date of Electronic Publication: 2022 Dec 16. |
| Spôsob vydávania: | Journal Article; Research Support, Non-U.S. Gov't |
| Jazyk: | English |
| Informácie o časopise: | Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004- |
| Výrazy zo slovníka MeSH: | Brain-Computer Interfaces*, Humans ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Electroencephalography/methods ; Imagery, Psychotherapy ; Algorithms |
| Abstrakt: | Objective. The brain-computer interface (BCI) system based on sensorimotor rhythm can convert the human spirit into instructions for machine control, and it is a new human-computer interaction system with broad applications. However, the spatial resolution of scalp electroencephalogram (EEG) is limited due to the presence of volume conduction effects. Therefore, it is very meaningful to explore intracranial activities in a noninvasive way and improve the spatial resolution of EEG. Meanwhile, low-delay decoding is an essential factor for the development of a real-time BCI system. Approach. In this paper, EEG conduction is modeled by using public head anatomical templates, and cortical EEG is obtained using dynamic parameter statistical mapping. To solve the problem of a large amount of computation caused by the increase in the number of channels, the filter bank common spatial pattern method is used to obtain a spatial filter kernel, which reduces the computational cost of feature extraction to a linear level. And the feature classification and selection of important features are completed using a neural network containing band-spatial-time domain self-attention mechanisms. Main results. The results show that the method proposed in this paper achieves high accuracy for the four types of motor imagery EEG classification tasks, with fairly low latency and high physiological interpretability. Significance. The proposed decoding framework facilitates the realization of low-latency human-computer interaction systems. (© 2022 IOP Publishing Ltd.) |
| Contributed Indexing: | Keywords: electroencephalogram (EEG); electrophysiological source imaging (ESI); filter bank common spatial pattern (FBCSP); motor imagery (MI); neural network (NN) |
| Entry Date(s): | Date Created: 20221221 Date Completed: 20221222 Latest Revision: 20230110 |
| Update Code: | 20250114 |
| DOI: | 10.1088/1741-2552/aca82d |
| PMID: | 36541542 |
| Databáza: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstrakt: | Objective. The brain-computer interface (BCI) system based on sensorimotor rhythm can convert the human spirit into instructions for machine control, and it is a new human-computer interaction system with broad applications. However, the spatial resolution of scalp electroencephalogram (EEG) is limited due to the presence of volume conduction effects. Therefore, it is very meaningful to explore intracranial activities in a noninvasive way and improve the spatial resolution of EEG. Meanwhile, low-delay decoding is an essential factor for the development of a real-time BCI system. Approach. In this paper, EEG conduction is modeled by using public head anatomical templates, and cortical EEG is obtained using dynamic parameter statistical mapping. To solve the problem of a large amount of computation caused by the increase in the number of channels, the filter bank common spatial pattern method is used to obtain a spatial filter kernel, which reduces the computational cost of feature extraction to a linear level. And the feature classification and selection of important features are completed using a neural network containing band-spatial-time domain self-attention mechanisms. Main results. The results show that the method proposed in this paper achieves high accuracy for the four types of motor imagery EEG classification tasks, with fairly low latency and high physiological interpretability. Significance. The proposed decoding framework facilitates the realization of low-latency human-computer interaction systems.<br /> (© 2022 IOP Publishing Ltd.) |
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| ISSN: | 1741-2552 |
| DOI: | 10.1088/1741-2552/aca82d |