  Accurate electroencephalogram (EEG) pattern decoding for specific mental
tasks is one of the key steps for the development of brain-computer interface
(BCI), which is quite challenging due to the considerably low signal-to-noise
ratio of EEG collected at the brain scalp. Machine learning provides a
promising technique to optimize EEG patterns toward better decoding accuracy.
However, existing algorithms do not effectively explore the underlying data
structure capturing the true EEG sample distribution, and hence can only yield
a suboptimal decoding accuracy. To uncover the intrinsic distribution structure
of EEG data, we propose a clustering-based multi-task feature learning
algorithm for improved EEG pattern decoding. Specifically, we perform affinity
propagation-based clustering to explore the subclasses (i.e., clusters) in each
of the original classes, and then assign each subclass a unique label based on
a one-versus-all encoding strategy. With the encoded label matrix, we devise a
novel multi-task learning algorithm by exploiting the subclass relationship to
jointly optimize the EEG pattern features from the uncovered subclasses. We
then train a linear support vector machine with the optimized features for EEG
pattern decoding. Extensive experimental studies are conducted on three EEG
datasets to validate the effectiveness of our algorithm in comparison with
other state-of-the-art approaches. The improved experimental results
demonstrate the outstanding superiority of our algorithm, suggesting its
prominent performance for EEG pattern decoding in BCI applications.
