IMPRESS: Informative Mutual Patch Representation for EEG Semi-Supervised Learning in Seizure Type Classification

Electroencephalogram (EEG) data annotation demands considerable expertise and is a time-intensive process. Moreover, inter-subject variability intensifies the challenge of domain shift, adversely impacting the generalization performance of deep learning models on unseen subjects. Current methods in EEG data analysis often struggle to handle the complex nature of brain activity without relying on EEG feature engineering. In this paper, we present a hybrid semi-supervised framework for seizure type classification, which relies on minimal domain knowledge provided by exploiting spectral and spatial patch-level representations of raw unlabeled EEG data, while leveraging a small amount of labeled data. Our method, IMPRESS, enhances EEG representation learning by combining multi-patch mutual information maximization with adversarial distribution alignment. We assessed the framework's performance for cross-patient seizure classification using publicly accessible Temple University Seizure Corpus. IMPRESS surpasses the best-performing semi-supervised learning method by 1.92% and 0.72% using balanced accuracy and macro-F1 metrics, respectively, with 40 labeled samples per class. Remarkably, IMPRESS surpasses the fully-supervised method while requiring only 25 labeled samples per class. Additionally, we visualize the learned feature embeddings, highlighting the underlying dynamics across different seizure types, aiding in understanding the model's behavior. This demonstrates the potential of leveraging multi-patch information from unlabeled data through a contrastive data-driven approach, alleviating the burden of annotating large amounts of EEG data.