braindecode.models.SignalJEPA

class braindecode.models.SignalJEPA(n_outputs=None, n_chans=None, chs_info=None, n_times=None, input_window_seconds=None, sfreq=None, *, feature_encoder__conv_layers_spec=((8, 32, 8), (16, 2, 2), (32, 2, 2), (64, 2, 2), (64, 2, 2)), drop_prob=0.0, feature_encoder__mode='default', feature_encoder__conv_bias=False, activation=<class 'torch.nn.modules.activation.GELU'>, pos_encoder__spat_dim=30, pos_encoder__time_dim=34, pos_encoder__sfreq_features=1.0, pos_encoder__spat_kwargs=None, transformer__d_model=64, transformer__num_encoder_layers=8, transformer__num_decoder_layers=4, transformer__nhead=8)

Architecture introduced in signal-JEPA for self-supervised pre-training, Guetschel, P et al (2024) [1]

Convolution Channel Foundation Model

This model is not meant for classification but for SSL pre-training. Its output shape depends on the input shape. For classification purposes, three variants of this model are available:

• SignalJEPA_Contextual

• SignalJEPA_PostLocal

• SignalJEPA_PreLocal

The classification architectures can either be instantiated from scratch (random parameters) or from a pre-trained SignalJEPA model.

Added in version 0.9.

References

[1]

Guetschel, P., Moreau, T., & Tangermann, M. (2024). S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention. In 9th Graz Brain-Computer Interface Conference, https://www.doi.org/10.3217/978-3-99161-014-4-003

Methods

forward(X, ch_idxs=None)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.