Beyond Patch-Level EEG Reconstruction: Contrastive Pretraining and Multiscale Convolutional Transformer for EEG Decoding

Self-supervised pretraining has shown early promise for improving non-invasive electroencephalogram (EEG) decoding. Recently, many large-scale EEG foundation models have converged on the recipe of raw signal tokenization followed by masked reconstruction pretraining. However, this approach may be suboptimal for data with low signal-to-noise ratio and sample-level dependencies at multiple timescales. To test this hypothesis, we develop a novel contrastive-pretrained EEG model with multiscale sample-level convolutions and Transformer encoder blocks (CoCoT-EEG). CoCoT achieves state-of-the-art performance on extensive benchmark decoding tasks with heterogeneous experimental configurations. Moreover, we systematically dissect the impact of architecture and pretraining objective through ablation experiments and mechanistic analyses. Consistent with our hypothesis, models benefit from contrastive pretraining, as well as sample-level feature extraction across patch boundaries and longer convolution kernels. Thus, we demonstrate the viability of contrastive learning for EEG modeling while suggesting key design considerations that complement existing architectural advances, prompting further investigations in alternative large-scale pretraining strategies.