EEG DINO Small — Self Distillation EEG Foundation Model EEG DINO Small encoder pretrained with DINO v2 hierarchical self distillation (Wang et al., MICCAI 2025). This is the eegdino small pretrained checkpoint for braindecode.models.EEGDINO , curated and re uploaded as part of the OpenEEG Bench effort. Quick start from pretrained reads both the architecture configuration ( config.json ) and the weights ( model.safetensors or pytorch model.bin ) and returns a ready to fine tune nn.Module . Model details Architecture braindecode.models.EEGDINO Expected channels 19 Expected sampling frequency 200 Hz Library braindecode ≥ 1.5 Loaded via huggingface hub.PyTorchModelHubMixin (free with braindecode[hub] ) For the full architecture description, parameter table, and references, see the rendered docstring at or in the interactive Model Explorer Space. Training data Temple University Hospital EEG Corpus (TUEG), 19 common 10 20 channels resampled to 200 Hz ( 9000 hours), following CBraMod's preprocessing. Pretrained by hierarchical self distillation. Intended use EEG feature extraction or fine tuning for downstream classification (e.g., TUEV, TUAB). The classification head is re initialized on…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy