DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI

Electroencephalography (EEG) foundation models promise general-purpose representations for Brain-Computer Interfaces (BCIs), yet most transfer effectively only under end-to-end fine-tuning and degrade markedly under frozen-probing—a protocol that diagnoses whether task-relevant information is directly accessible in a fixed representation. We attribute this gap to the reliance on generic sequence architectures that overlook the biophysical and dynamical structure of neural activity. We propose DeeperBrain, an EEG foundation model that injects neurophysiological inductive biases at two levels. Architecturally, a volume-conduction-aware channel encoding models spatial mixing from 3D electrode geometry, and a neurodynamics-aware temporal encoding captures slow oscillations and adaptive decay. For pretraining, we combine Masked EEG Reconstruction (MER) with Neurodynamics Statistics Prediction (NSP), an auxiliary self-supervised objective whose targets—spectral power, phase synchronization, cross-frequency coupling, and signal complexity—are computed directly from the signal and aligned with established neurophysiological descriptors. Pretrained on over 17,200 hours of heterogeneous EEG and evaluated on 12 downstream datasets spanning emotion, motor, sleep, seizure, speech, disorder, vigilance, and workload tasks, DeeperBrain achieves state-of-the-art or competitive fine-tuning performance and, notably, consistently superior frozen-probing performance. The results indicate that neurophysiological priors make downstream information more accessible without backbone adaptation, a property we view as a necessary step toward universal BCI. Our code will be publicly available on Github.