Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

TL;DR

This paper introduces a transformer-based EEG decoding method that effectively captures global spatial-temporal dependencies, outperforming CNN-based approaches with fewer parameters, and advancing brain-computer interface technology.

Contribution

It is the first comprehensive transformer-based approach for EEG decoding, enhancing spatial-temporal feature extraction and achieving state-of-the-art classification performance.

Findings

Achieved state-of-the-art multi-class EEG classification accuracy.

Utilized fewer parameters than existing CNN-based methods.

Effectively captured global dependencies in EEG data.

Abstract

At present, people usually use some methods based on convolutional neural networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in perceiving global dependencies, which is not adequate for common EEG paradigms with a strong overall relationship. Regarding this issue, we propose a novel EEG decoding method that mainly relies on the attention mechanism. The EEG data is firstly preprocessed and spatially filtered. And then, we apply attention transforming on the feature-channel dimension so that the model can enhance more relevant spatial features. The most crucial step is to slice the data in the time dimension for attention transforming, and finally obtain a highly distinguishable representation. At this time, global averaging pooling and a simple fully-connected layer are used to classify different categories of EEG data. Experiments on two public datasets indicate that the strategy of attention transforming effectively utilizes spatial and temporal features. And we have reached the level of the state-of-the-art in multi-classification of EEG, with fewer parameters. As far as we know, it is the first time that a detailed and complete method based on the transformer idea has been proposed in this field. It has good potential to promote the practicality of brain-computer interface (BCI). The source code can be found at: \textit{https://github.com/anranknight/EEG-Transformer}.