Objective To explore a motor-related brain computer interface(BCI)technology based on the decoding of six precise hand movements.Methods Based on the analysis of the mechanisms and response characteristics of six common types of fine motor electroencephalography(EEG)in the hand,a BCI paradigm for fine motor execution was designed.A motion related EEG signal decoding model based on convolutional neural networks was implemented,and a BCI system based on fine motor was built.Six types of fine motor gesture EEG signals from eight healthy subjects and two patients with significant motor dysfunction due to lesions involving the parietal lobe were classified.Results The classification accuracy of EEG signals in 10 subjects under the BCI system based on fine hand movements was(79.20±6.05)%.Conclusions The BCI method based on decoding six types of fine hand movements has certain effectiveness and generalization ability.
precise hand movementbrain computer interfaceEEGdecoding method
国家科技期刊平台
NSTL
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