Decoding Human Attentive States from Spatial-temporal EEG Patches Using Transformers

TL;DR

This paper presents EEG-PatchFormer, a transformer-based deep learning model that effectively decodes human attention states from EEG data by capturing spatial-temporal features, outperforming existing methods on benchmark datasets.

Contribution

Introduces EEG-PatchFormer, a novel transformer-based framework combining CNNs and patching modules for improved EEG attention classification in BCI applications.

Findings

Achieves higher accuracy than existing models

Improves AUC and macro-F1 scores

Demonstrates effective spatial-temporal feature learning

Abstract

Learning the spatial topology of electroencephalogram (EEG) channels and their temporal dynamics is crucial for decoding attention states. This paper introduces EEG-PatchFormer, a transformer-based deep learning framework designed specifically for EEG attention classification in Brain-Computer Interface (BCI) applications. By integrating a Temporal CNN for frequency-based EEG feature extraction, a pointwise CNN for feature enhancement, and Spatial and Temporal Patching modules for organizing features into spatial-temporal patches, EEG-PatchFormer jointly learns spatial-temporal information from EEG data. Leveraging the global learning capabilities of the self-attention mechanism, it captures essential features across brain regions over time, thereby enhancing EEG data decoding performance. Demonstrating superior performance, EEG-PatchFormer surpasses existing benchmarks in accuracy, area under the ROC curve (AUC), and macro-F1 score on a public cognitive attention dataset. The code can be found via: https://github.com/yi-ding-cs/EEG-PatchFormer .