CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection
This work addresses the challenge of analyzing high-dimensional EEG signals for detecting abnormal brain activity, with potential applications in neuroscience and clinical practice, though it appears incremental in combining existing techniques.
The paper tackles EEG abnormality detection by proposing CwA-T, a framework combining a channelwise autoencoder with a transformer classifier, achieving 85.0% accuracy, 76.2% sensitivity, and 91.2% specificity on the TUH Abnormal EEG Corpus.
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.