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

TL;DR

This paper introduces a versatile EEG classification framework using reinforced attention and convolutional mapping, achieving high accuracy across multiple brain signal analysis tasks with low latency.

Contribution

The study presents a novel generic EEG classification framework that automatically selects informative signals and uncovers spatial dependencies, outperforming existing methods across various applications.

Findings

Achieves over 97% accuracy on multiple datasets

Outperforms state-of-the-art baselines

Demonstrates robustness and low latency in EEG classification

Abstract

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.