  Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG)
signals can enhance the performance of the brain-computer interface (BCI)
system and reduce the computational burden required for processing lengthy
MI-EEG signals. In this paper, we propose an unsupervised method based on the
self-attention mechanism to detect the salient intervals of MI-EEG signals
automatically. Our suggested method can be used as a preprocessing step within
any BCI algorithm to enhance its performance. The effectiveness of the
suggested method is evaluated on the most widely used BCI algorithm, the common
spatial pattern (CSP) algorithm, using dataset 2a from BCI competition IV. The
results indicate that the proposed method can effectively prune MI-EEG signals
and significantly enhance the performance of the CSP algorithm in terms of
classification accuracy.
