Feature-Enriched Mutual Distillation with Semi-Supervised Contrastive Learning for Electroencephalogram Signal Classification

Semi-supervised learning (SSL) has gained remarkable traction for its capacity to utilize extensive unlabeled datasets while relying on minimal labeled samples, thereby addressing the limitations of annotation scarcity. This study introduces an innovative framework, Feature-Enriched Mutual Distillation with Semi-Supervised Contrastive Learning (FMDSSCL), specifically designed for classifying Electroencephalogram (EEG) signals. The proposed approach integrates four foundational SSL strategies, i.e., pseudo-labeling, entropy minimization, generic regularization, and consistency regularization, into a cohesive framework augmented by feature-based mutual distillation and contrastive learning (CL), unified under a novel loss function. Feature-based mutual distillation enables cross-layer knowledge transfer, significantly improving feature extraction capabilities across model layers. Simultaneously, CL emphasizes identifying commonalities in representations across identical instances and supplementary data samples. To further enhance the exploration of EEG signal characteristics, the framework employs a fully convolutional network (FCN) comprising three convolutional blocks, effectively capturing both localized and global temporal patterns. The efficacy of FMDSSCL is demonstrated through comprehensive evaluations on four benchmark datasets, i.e., FingerMovements, MotorImagery, PenDigits, and SelfRegulationSCP, where it outperforms a variety of established semi-supervised and fully supervised approaches, solidifying its contribution to SSL research in EEG analysis.