Signal JEPA Self supervised pre trained weights for the Signal JEPA foundation model from Guetschel et al. (2024), packaged for use with braindecode. See the full API reference in the docs: braindecode.models.SignalJEPA . The model was pre trained on the Lee2019 dataset (62 EEG channels in the 10 10 layout, sampled at 128 Hz). The repo ships the weights together with a config.json so they can be loaded in one line with YourModelClass.from pretrained(repo id, ...) . Available checkpoints Two variants are published: repo ID channel embedding included when to use braindecode/signal jepa ✓ 62 row ChannelEmbedding aligned with the pre training layout your recording channels are a subset (by name, case insensitive) of the 62 pre training channels — you want to reuse the learned spatial embeddings braindecode/signal jepa without chans ✗ only the SSL backbone (feature encoder + transformer) your channels are not a subset of the pre training set, or you prefer to train channel embeddings from scratch If you are unsure, start with braindecode/signal jepa without chans : it always works, regardless of your electrode layout. Quick start Base model (pre training architecture) The base model out…
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