  Pre-movement decoding plays an important role in movement detection and is
able to detect movement onset with low-frequency electroencephalogram (EEG)
signals before the limb moves. In related studies, pre-movement decoding with
standard task-related component analysis (STRCA) has been demonstrated to be
efficient for classification between movement state and resting state. However,
the accuracies of STRCA differ among subbands in the frequency domain. Due to
individual differences, the best subband differs among subjects and is
difficult to be determined. This study aims to improve the performance of the
STRCA method by a feature selection on multiple subbands and avoid the
selection of best subbands. This study first compares three frequency range
settings ($M_1$: subbands with equally spaced bandwidths; $M_2$: subbands whose
high cut-off frequencies are twice the low cut-off frequencies; $M_3$: subbands
that start at some specific fixed frequencies and end at the frequencies in an
arithmetic sequence.). Then, we develop a mutual information based technique to
select the features in these subbands. A binary support vector machine
classifier is used to classify the selected essential features. The results
show that $M_3$ is a better setting than the other two settings. With the
filter banks in $M_3$, the classification accuracy of the proposed FBTRCA
achieves 0.8700$\pm$0.1022, which means a significantly improved performance
compared to STRCA (0.8287$\pm$0.1101) as well as to the cross validation and
testing method (0.8431$\pm$0.1078).
