A motor imagery analysis algorithm based on spatio-temporal-frequency joint selection and relevance vector machine
WANG Hong-tao
LI Ting
HUANG Hui
HE Yue-bang
LIU Xu-cheng
Abstract：Convergent studies have reported inter-subject variability in EEG representation when subjects performed same cognitive tasks, yielding a significant drawback for developing a practical BCI system. In order to address this problem, we have introduced a subject-dependent specio-temporal-frequecy joint feature selection method. Specifically, we first selected 55-channel EEG signals among the original 118-channel recordings according to the close relevance of the signals in motor-related areas. A 7-fold cross validation approach was applied to select the optimal time-window and frequency bands, which match individual subject based upon the training data set. Then motor imagery related features were determined via the common spatial pattern method. The obtained subject-dependent features were feeded to a relevance vector machine for motor imagery classification. The experiment results show that our framework demonstrated superior performance as showing in the higher classification accuracy (94.49％in comparison with the highest classification accuracy 94.17％) in the competition III. Compared with the other three existing methods, our method also has obvious advantages. In summary, we provided feasible framework to account for inter-subject variability, which would be a new method for the designing of the online motor imagery brain computer interface system.
Keywords：brain-computer interfacemotor imagerycommon spatial pattenrelevance vector machine
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1403-1408 )