  Electroencephalography (EEG) signals reflect activities on certain brain
areas. Effective classification of time-varying EEG signals is still
challenging. First, EEG signal processing and feature engineering are
time-consuming and highly rely on expert knowledge. In addition, most existing
studies focus on domain-specific classification algorithms which may not be
applicable to other domains. Moreover, the EEG signal usually has a low
signal-to-noise ratio and can be easily corrupted. In this regard, we propose a
generic EEG signal classification framework that accommodates a wide range of
applications to address the aforementioned issues. The proposed framework
develops a reinforced selective attention model to automatically choose the
distinctive information among the raw EEG signals. A convolutional mapping
operation is employed to dynamically transform the selected information to an
over-complete feature space, wherein implicit spatial dependency of EEG samples
distribution is able to be uncovered. We demonstrate the effectiveness of the
proposed framework using three representative scenarios: intention recognition
with motor imagery EEG, person identification, and neurological diagnosis.
Three widely used public datasets and a local dataset are used for our
evaluation. The experiments show that our framework outperforms the
state-of-the-art baselines and achieves the accuracy of more than 97% on all
the datasets with low latency and good resilience of handling complex EEG
signals across various domains. These results confirm the suitability of the
proposed generic approach for a range of problems in the realm of
Brain-Computer Interface applications.
