Author/Authors :
Zheng, Xuanci Tongji University - Shanghai, China , Li, Jie Tongji University - Shanghai, China , Ji, Hongfei Tongji University - Shanghai, China , Duan, Lili Tongji University - Shanghai, China , Li, Maozhen Department of Electronic and Computer Engineering - Brunel University London - Uxbridge, UK , Pang, Zilong Tongji University - Shanghai, China , Zhuang, Jie School of Psychology - Shanghai University of Sport - Shanghai, China , Rongrong, Lu Department of Rehabilitation - Huashan Hospital - Fudan University - Shanghai, China , Tianhao, Gao Department of Rehabilitation - Huashan Hospital - Fudan University - Shanghai, China
Abstract :
The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration
time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations
of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning
so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data
from traditional commands and transfer patterns through the data from new commands. Through the comparison of the
average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we
find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets.
Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the
information from source data. We enlarge the command set while shortening the calibration time, which is of significant
importance to the MI-BCI application.
Keywords :
Transfer , BCI , EEG , Classification
