In this work, we present a comprehensive research program leveraging EEG foundation models to unlock the development of new EEG-based biomarkers for neurological pathologies.

- Seizure detection on public Temple University EEG Seizure database (TUSZ) (n=7,152 windows)
- Normal/abnormal classification on both proprietary data (n=15,285), and public Temple University Abnormal (TUAB) database (n=23,040),
- Multiclass pathology differentiation on proprietary data (n=15,285) with 4 labels ( Normal/Lesion/Status Epilepticus/Encephalopathy). 

On seizure detection task, the model achieved an AUROC at 0.926 ± 0.002 with a sensitivity at 0.909 ± 0.035. For normal/abnormal classification, the AUROC reached 0.970 ± 0.001 on proprietary database and 0.910 ± 0.002 for the public one. Finally, the multiclass pathology differentiation obtained a Weighted F1 at 0.730 ± 0.001. On these tasks our model achieved or surpassed state-of-the-art results. 

Importantly, in low-data regimes (<10% of available data), the model improved AUPRC by 2–17%, demonstrating strong data efficiency. 

Ongoing work focuses on leveraging EEG foundation models as a universal backbone for diverse EEG diagnostic applications, including abnormality subtype classification, seizure event identification, etc.... Our findings demonstrate that foundation models can catalyze the development of versatile, resource-efficient, and clinically relevant EEG analysis systems, paving the way toward scalable AI-assisted neurology.