  Background: Common spatial pattern (CSP) has been widely used for feature
extraction in the case of motor imagery (MI) electroencephalogram (EEG)
recordings and in MI classification of brain-computer interface (BCI)
applications. BCI usually requires relatively long EEG data for reliable
classifier training. More specifically, before using general spatial patterns
for feature extraction, a training dictionary from two different classes is
used to construct a compound dictionary matrix, and the representation of the
test samples in the filter band is estimated as a linear combination of the
columns in the dictionary matrix. New method: To alleviate the problem of
sparse small sample (SS) between frequency bands. We propose a novel sparse
group filter bank model (SGFB) for motor imagery in BCI system. Results: We
perform a task by representing residuals based on the categories corresponding
to the non-zero correlation coefficients. Besides, we also perform joint sparse
optimization with constrained filter bands in three different time windows to
extract robust CSP features in a multi-task learning framework. To verify the
effectiveness of our model, we conduct an experiment on the public EEG dataset
of BCI competition to compare it with other competitive methods. Comparison
with existing methods: Decent classification performance for different subbands
confirms that our algorithm is a promising candidate for improving MI-based BCI
performance.
