CoCL: EEG connectivity-guided contrastive learning for seizure detection

Epilepsy is a neurological disorder characterized by repetitive seizures, making early prediction crucial for patient safety and quality of life. Traditional detection methods primarily rely on time-frequency information from EEG signals. However, since EEG signals are interconnected and abnormal activity spreads across brain regions, understanding their connectivity is essential. This study proposes CoCL, a novel representation learning approach that employs contrastive learning with EEG connectivity-guided supervision to capture these interconnections. When applied during pretraining and transferred to seizure detection, CoCL outperforms state-of-the-art methods and maintains high accuracy with only 6 EEG channels, reducing the need for numerous electrodes.