Know Your Mind: Adaptive Brain Signal Classification with Reinforced Attentive Convolutional Neural Networks

Electroencephalography (EEG) signals reƒect activities on certain brain areas. E‚ective classi€cation 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-speci€c classi€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 classi€cation framework that accommodates a wide range of applications to address the aforementioned issues. Œe proposed framework develops a reinforced selective aŠention 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 e‚ectiveness of the proposed framework using three representative scenarios: intention recognition with motor imagery EEG, person identi€cation, and neurological diagnosis. Œree widely used public datasets and a local dataset are used for our evaluation. Œe 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. Œese results con€rm the suitability of the proposed generic approach for a range of problems in the realm of Brain-Computer Interface applications.