CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection

TL;DR

CwA-T is an efficient EEG abnormality detection framework combining a channelwise autoencoder and transformer classifier, achieving high accuracy with low computational cost and maintaining interpretability.

Contribution

It introduces a novel channelwise autoencoder with a transformer classifier for EEG analysis, improving efficiency and interpretability over existing methods.

Findings

Achieves 85.0% accuracy on TUH EEG corpus

Requires only 202M FLOPs and 2.9M parameters

Outperforms baseline models like EEGNet and Deep4Conv

Abstract

Electroencephalogram (EEG) signals are critical for detecting abnormal brain activity, but their high dimensionality and complexity pose significant challenges for effective analysis. In this paper, we propose CwA-T, a novel framework that combines a channelwise CNN-based autoencoder with a single-head transformer classifier for efficient EEG abnormality detection. The channelwise autoencoder compresses raw EEG signals while preserving channel independence, reducing computational costs and retaining biologically meaningful features. The compressed representations are then fed into the transformer-based classifier, which efficiently models long-term dependencies to distinguish between normal and abnormal signals. Evaluated on the TUH Abnormal EEG Corpus, the proposed model achieves 85.0% accuracy, 76.2% sensitivity, and 91.2% specificity at the per-case level, outperforming baseline models such as EEGNet, Deep4Conv, and FusionCNN. Furthermore, CwA-T requires only 202M FLOPs and 2.9M parameters, making it significantly more efficient than transformer-based alternatives. The framework retains interpretability through its channelwise design, demonstrating great potential for future applications in neuroscience research and clinical practice. The source code is available at https://github.com/YossiZhao/CAE-T.