Journal of System Simulation
Abstract
Abstract: EEG-based BCI system can help the daily life and rehabilitation training of limb movement disorders patients. Due to the low signal-to-noise ratio and large individual differences of EEG signals, the accuracy and efficiency of EEG feature extraction and classification are not high, which affects the wide application of online BCI system. A CNN with spatial information is proposed for the online classification of MI-EEG signals. The reordered MI-EEG is convolved horizontally and vertically respectively. With the contralateral effect of motor imagery ERD/ERS phenomenon, the spatial information in MI-EEG is fully utilized to achieve the real-time acquisition and classification of MI-EEG signals. Experimental results show that the proposed method is effectively performed in real time, which provide a basis for the implementation of online MI-BCI system.
Recommended Citation
Yang, Fengwei; Chen, Peng; Xi, Kai; Pu, Hualin; and Liu, Xueyin
(2023)
"Online Classification Method for Motor Imagery EEG with Spatial Information,"
Journal of System Simulation: Vol. 35:
Iss.
2, Article 3.
DOI: 10.16182/j.issn1004731x.joss.21-0894
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol35/iss2/3
First Page
254
Revised Date
2021-11-12
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.21-0894
Last Page
267
CLC
TP391.9
Recommended Citation
Fengwei Yang, Peng Chen, Kai Xi, Hualin Pu, Xueyin Liu. Online Classification Method for Motor Imagery EEG with Spatial Information[J]. Journal of System Simulation, 2023, 35(2): 254-267.
DOI
10.16182/j.issn1004731x.joss.21-0894
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