Feature Selection via Dynamic Graph-based Attention Block in MI-based EEG Signals

TL;DR

This paper introduces a novel end-to-end deep preprocessing approach with a dynamic graph-based attention block to improve feature extraction from EEG signals in MI-based BCI, enhancing classification robustness and discriminability.

Contribution

The paper presents a new deep preprocessing method incorporating temporal, spatial, graph, and similarity blocks, specifically designed to enhance MI-related features in EEG signals.

Findings

Improved classification performance on BCI dataset 2a.

More discriminative and clustered feature distributions.

Enhanced robustness of MI feature extraction.

Abstract

Brain-computer interface (BCI) technology enables direct interaction between humans and computers by analyzing brain signals. Electroencephalogram (EEG) is one of the non-invasive tools used in BCI systems, providing high temporal resolution for real-time applications. However, EEG signals are often affected by a low signal-to-noise ratio, physiological artifacts, and individual variability, representing challenges in extracting distinct features. Also, motor imagery (MI)-based EEG signals could contain features with low correlation to MI characteristics, which might cause the weights of the deep model to become biased towards those features. To address these problems, we proposed the end-to-end deep preprocessing method that effectively enhances MI characteristics while attenuating features with low correlation to MI characteristics. The proposed method consisted of the temporal, spatial, graph, and similarity blocks to preprocess MI-based EEG signals, aiming to extract more discriminative features and improve the robustness. We evaluated the proposed method using the public dataset 2a of BCI Competition IV to compare the performances when integrating the proposed method into the conventional models, including the DeepConvNet, the M-ShallowConvNet, and the EEGNet. The experimental results showed that the proposed method could achieve the improved performances and lead to more clustered feature distributions of MI tasks. Hence, we demonstrated that our proposed method could enhance discriminative features related to MI characteristics.