Research on the Online Recognition of the Motion Image of the Adjacent Joints of the Lower Limbs.

At present, offline analysis and research of lower limb motor imagery brain computer interface (MI-BCI) are relatively mature, but there are few researches on the online recognition of lower limb MI-BCI. The online recognition of the two MI tasks of the knee-ankle adjacent joints of the right lower extremity was carried out, and the EEG signals of the two MI tasks assisted by electrical stimulation are collected. The Filter Bank Common Spatial Pattern (FBCSP) is used for feature extraction, and BP neural network is used for online recognition. The average recognition accuracy rate of online motor imagery of 13 subjects reaches 79.62%, and the recognition accuracy rate of the highest person is 92.50%, which verified the feasibility and practicability of online MI-BCI in adjacent joints of lower limbs.