Models¶
This section documents the foundation models shipped with NeuralBench and
the quirks (channel-mapping assumptions, pretraining-data overlap, dropout
overrides) that need to be considered when evaluating them. Task-specific
architectures (eegnet, deep4net, eegconformer, atcnet,
bdtcn, ctnet, shallow_fbcsp_net, simpleconv_time_agg) are
off-the-shelf braindecode models with no NeuralBench-specific configuration;
see their upstream documentation or models/<name>.yaml for hyperparameters.