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neuraltrain.models.reve.NtReve

pydantic model neuraltrain.models.reve.NtReve

Config for the braindecode REVE model with channel-mapping support.

Extends BaseBrainDecodeModel with REVE-specific logic:

1. Channel remapping – an explicit channel_mapping dict maps dataset channel names to REVE position-bank names. Channels whose names already appear in the bank (exact match) need no entry.

2. Pretrained loading – bypasses the base-class restriction on n_times for pretrained models, since REVE needs it to size its final_layer.

3. Encoder-only output – when n_outputs is None (downstream wrapper handles the head), the model is wrapped to call forward(return_output=True) and return the final transformer layer output, bypassing REVE’s final_layer.

Parameters:

channel_mapping (dict or None) – Explicit mapping from dataset channel names to REVE position-bank names. Useful for EEG systems whose naming convention is absent from the bank (e.g. Neuromag "EEG 005" or easycap-M10 numeric "2").

Fields:

• channel_mapping (dict[str, str] | None)

chs_info_required: ClassVar[bool] = True

needs_n_times: ClassVar[bool] = True

field channel_mapping: dict[str, str] | None = None

build(n_chans: int | None = None, n_times: int | None = None, n_outputs: int | None = None, chs_info: list[dict[str, Any]] | None = None, **kwargs: Any) → Module

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