Some predefined network architectures for EEG decoding.

Models Summary#

This page offers a summary of many implemented models. Please note that this list may not be exhaustive. For the definitive and most current list, including detailed class documentation, please consult the API documentation.

We are continually expanding this collection and welcome contributions! If you have implemented a model relevant to EEG, EcoG, or MEG analysis, consider adding it to Braindecode. See the “Submit a New Model” section below for details.

Braindecode Models — Visualization comparing the models based on their total number of parameters (left plot) and the primary experimental paradigm they were designed for (right plot).#

Columns definitions:

Model: The name of the model.
Paradigm: The paradigm(s) the model is typically used for (e.g., Motor Imagery, P300, Sleep Staging). ‘General’ indicates applicability across multiple paradigms or no specific paradigm focus.
Type: The model’s primary function (e.g., Classification, Regression, Embedding).
Freq (Hz): The data sampling rate (in Hertz) the model is designed for. Note that this might be adaptable depending on the specific dataset and application.
Hyperparameters: The mandatory hyperparameters required for instantiating the model class. These may include n_chans (number of channels), n_outputs (number of output classes or regression targets), n_times (number of time points in the input window), or sfreq (sampling frequency). Also, n_times can be derived implicitly by providing both sfreq and input_window_seconds.
#Parameters: The approximate total number of trainable parameters in the model, calculated using a consistent configuration (see note below).

Filter models:

Model	Paradigm	Type	Freq(Hz)	Hyperparameters	#Parameters
ATCNet	General	Classification	250	n_chans, n_outputs, n_times	113732
AttentionBaseNet	Motor Imagery	Classification	250	n_chans, n_outputs, n_times	3692
BDTCN	Normal/Abnormal	Classification	100	n_chans, n_outputs, n_times	456502
BIOT	Sleep Staging Epilepsy	Classification	200	n_chans, n_outputs	3183879
ContraWR	Sleep Staging	Classification, Embedding	125	n_chans, n_outputs, sfreq	1160165
CTNet	Motor Imagery	Classification	250	n_chans, n_outputs, n_times	26900
Deep4Net	Motor Imagery	Classification	250	n_chans, n_outputs, n_times	282879
DeepSleepNet	Sleep Staging	Classification	256	n_chans, n_outputs	24744837
EEGConformer	General	Classification	250	n_chans, n_outputs, n_times	789572
EEGInceptionERP	ERP SSVEP	Classification	128	n_chans, n_outputs	14926
EEGInceptionMI	Motor Imagery	Classification	250	n_chans, n_outputs, n_times	558028
EEGITNet	Motor Imagery	Classification	125	n_chans, n_outputs, n_times	5212
EEGMiner	Emotion Recognition	Classification	128	n_chans, n_outputs, n_times, sfreq	7572
EEGNetv1	General	Classification	128	n_chans, n_outputs, n_times	3052
EEGNetv4	General	Classification	128	n_chans, n_outputs, n_times	2484
EEGNeX	Motor Imagery	Classification	125	n_chans, n_outputs, n_times	55940
EEGResNet	General	Classification	250	n_chans, n_outputs, n_times	247484
EEGSimpleConv	Motor Imagery	Classification	80	n_chans, n_outputs, sfreq	730404
EEGTCNet	Motor Imagery	Classification	250	n_chans, n_outputs	4516
Labram	General	Classification, Embedding	200	n_chans, n_outputs, n_times	5866180
MSVTNet	Motor Imagery	Classification	250	n_chans, n_outputs, n_times	75494
SCCNet	Motor Imagery	Classification	125	n_chans, n_outputs, n_times, sfreq	12070
ShallowFBCSPNet	Motor Imagery	Classification	250	n_chans, n_outputs, n_times	46084
SignalJEPA	Motor Imagery ERP SSVEP	Embedding	128	n_times, chs_info	3456882
SignalJEPA_Contextual	Motor Imagery ERP SSVEP	Classification	128	n_outputs, n_times, chs_info	3459184
SignalJEPA_PostLocal	Motor Imagery ERP SSVEP	Classification	128	n_chans, n_outputs, n_times	16142
SignalJEPA_PreLocal	Motor Imagery ERP SSVEP	Classification	128	n_outputs, n_times, chs_info	16142
SincShallowNet	Motor Imagery	Classification	250	n_chans, n_outputs, n_times, sfreq	21892
SleepStagerBlanco2020	Sleep Staging	Classification	100	n_chans, n_outputs, n_times	2845
SleepStagerChambon2018	Sleep Staging	Classification	128	n_chans, n_outputs, n_times, sfreq	5835
SleepStagerEldele2021	Sleep Staging	Classification	100	n_chans, n_outputs, n_times, sfreq	719925
SPARCNet	Epilepsy	Classification	200	n_chans, n_outputs, n_times	1141921
SyncNet	Emotion Recognition Alcoholism	Classification	256	n_chans, n_outputs, n_times	554
TIDNet	General	Classification	250	n_chans, n_outputs, n_times	240404
TSceptionV1	Emotion Recognition	Classification	256	n_chans, n_outputs, n_times, sfreq	2187206
USleep	Sleep Staging	Classification	128	n_chans, n_outputs, n_times, sfreq	2482011

The parameter counts shown in the table were calculated using consistent hyperparameters for models within the same paradigm, based largely on Braindecode’s default implementation values. These counts provide a relative comparison but may differ from those reported in the original publications due to variations in specific architectural details, input dimensions used in the paper, or calculation methods.

Submit a new model#

Want to contribute a new model to Braindecode? Great! You can propose a new model by opening an issue (please include a link to the relevant publication or description) or, even better, directly submit your implementation via a pull request. We appreciate your contributions to expanding the library!