Know Your Mind: Adaptive Brain Signal Classification with Reinforced Attentive Convolutional Neural Networks
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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 engineer- ing are time-consuming and highly rely on expert knowledge. In addition, most existing studies focus on domain-specific classifi- cation 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 auto- matically choose the distinctive information among the raw EEG signals. A convolutional mapping operation is employed to dy- namically 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 effec- tiveness 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 experi- ments show that our framework outperforms the state-of-the-art baselines and achieves the accuracy of more than 97 the datasets with low latency and good resilience of handling complex EEG signals across various domains. These results confirm the suit- ability of the proposed generic approach for a range of problems in the realm of Brain-Computer Interface applications.
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