Mu rhythm-based cursor control: an offline analysis.
OBJECTIVE
To classify the EEG data recorded in mu rhythm-based cursor control experiments with 4 possible choices.


METHODS
The algorithm included preprocessing, feature extraction, and classification. Two spatial filters, common average reference and common spatial subspace decomposition, were used in preprocessing to improve the signal-to-noise ratio, and then two features were extracted based on the power spectrum and the time course of the mu rhythm respectively. A Fisher ratio was defined to select channels in feature extraction. A 2-dimensional linear classifier was trained for final classification.


RESULTS
Two types of classifiers were trained for the training dataset. The uniform classifier gave a classification accuracy of 76.4%, and the classifier trained by leave-one-out method gave a classification accuracy of 74.4%, both higher than the online accuracy 69.5%. The uniform classifier was applied to the test dataset and the classification accuracy was 65.9%, lower than the online accuracy 73.2%.


CONCLUSIONS
Spatial filtering can give a notable improvement in classification accuracy. The time course of the mu rhythm, as well as the power of the mu rhythm, shows difference between the 4 targets, and can contribute to the classification.


CONCLUSIONS
The spatial filtering, feature extraction and channel selection methods in the algorithm will provide some practical suggestions for further study on the mu rhythm-based brain-computer interface.
Cheng
Ming
M
Department of Biomedical Engineering, Tsinghua University, Beijing 100084 China. chm99@mails.tsinghua.edu.cn
Jia
Wenyan
W
Gao
Xiaorong
X
Gao
Shangkai
S
Yang
Fusheng
F
D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009044:Motor Cortex / Q000502:physiology*; D013003:Somatosensory Cortex / Q000502:physiology*