Promises and Perils of EEG Foundation Models: On the Role of Pretraining

Foundation models for electroencephalography (EEG) typically rely on masked prediction, with the promise that pretraining on large unlabeled collections yields general-purpose representations. Yet published evaluations rarely isolate what pretraining itself contributes, leaving open whether reported gains come from representations learned from unlabeled EEG or from the encoder and adaptation protocol. We ask what pretraining contributes, whether its benefits are task-dependent, and whether the pretext task is aligned with all downstream tasks or only with some. Across 23 published encoder configurations and 34 datasets, evaluated with frozen weights, we find that pretraining gains are uneven: substantial for sleep staging and clinical EEG, but small or absent for motor imagery and event-related potentials, and almost entirely diminished once classifiers also receive handcrafted spectral and covariance features. To understand why, we pretrain encoders on transformed EEG that preserves only selected statistics of the original signals. Transformations retaining spectra and cross-spectra alone reproduce the benefits of original-EEG pretraining, yet the same transformation substantially impairs supervised motor-imagery and ERP decoding. Finally, adding a supervised term to reconstruction shows these representations to be learnable from the same data at negligible cost to the pretext objective; but removing supervision erodes most of the acquired decoding gains with little change in reconstruction loss. In the configurations studied, good reconstruction therefore does not assure the acquisition or retention of representations useful for motor-imagery and ERP decoding. These findings highlight objective–task alignment as a central challenge for EEG pretraining, motivating objectives better aligned with downstream tasks.