braindecode.models.EEGModuleMixin

class braindecode.models.EEGModuleMixin(n_outputs=None, n_chans=None, chs_info=None, n_times=None, input_window_seconds=None, sfreq=None)

Mixin class for all EEG models in braindecode.

This class integrates with Hugging Face Hub when the huggingface_hub package is installed, enabling models to be pushed to and loaded from the Hub using push_to_hub() and from_pretrained() methods.

Parameters:

• n_outputs (int) – Number of outputs of the model. This is the number of classes in the case of classification.

• n_chans (int) – Number of EEG channels.

• chs_info (list of dict) – Information about each individual EEG channel. This should be filled with info["chs"]. Refer to mne.Info for more details.

• n_times (int) – Number of time samples of the input window.

• input_window_seconds (float) – Length of the input window in seconds.

• sfreq (float) – Sampling frequency of the EEG recordings.

Raises:

ValueError – If some input signal-related parameters are not specified: and can not be inferred.

Notes

If some input signal-related parameters are not specified, there will be an attempt to infer them from the other parameters.

Methods

classmethod from_config(config)

Create a model instance from a configuration dict.

This is the inverse of get_config(). Weights are not loaded – use from_pretrained() for that.

Parameters:

config (dict) – Configuration dict as returned by get_config().

Returns:

A new model instance.

Return type:

EEGModuleMixin

Examples

>>> import json
>>> from braindecode.models import EEGNet
>>> model = EEGNet(n_chans=22, n_times=1000, n_outputs=4, F1=16)
>>> config = model.get_config()
>>> # Reconstruct (without weights)
>>> model2 = EEGNet.from_config(config)
>>> model2.F1
16
>>> # Or from a JSON file
>>> with open("config.json") as f:
...     config = json.load(f)
>>> model3 = EEGNet.from_config(config)

Added in version 1.4.

get_config()

Return a JSON-serializable dict of all __init__ parameters.

The returned dictionary can be saved to a JSON file and later used with from_config() to reconstruct the model (without weights). It is also used internally by push_to_hub() to persist the full model configuration.

Returns:

All __init__ parameters, JSON-serializable. type[nn.Module] parameters (e.g. activation) are encoded as importable dotted-path strings.

Return type:

dict

Examples

>>> import json
>>> from braindecode.models import EEGNet
>>> model = EEGNet(n_chans=22, n_times=1000, n_outputs=4, F1=16)
>>> config = model.get_config()
>>> config["F1"]
16
>>> # Save to disk
>>> with open("config.json", "w") as f:
...     json.dump(config, f)

Added in version 1.4.

get_output_shape()

Returns shape of neural network output for batch size equal 1.

Returns:

output_shape – shape of the network output for batch_size==1 (1, …)

Return type:

tuple[int, …]

get_torchinfo_statistics(col_names=('input_size', 'output_size', 'num_params', 'kernel_size'), row_settings=('var_names', 'depth'))

Generate table describing the model using torchinfo.summary.

Parameters:

• col_names (tuple, optional) – Specify which columns to show in the output, see torchinfo for details, by default (“input_size”, “output_size”, “num_params”, “kernel_size”)

• row_settings (tuple, optional) – Specify which features to show in a row, see torchinfo for details, by default (“var_names”, “depth”)

Returns:

ModelStatistics generated by torchinfo.summary.

Return type:

torchinfo.ModelStatistics

load_state_dict(state_dict, *args, **kwargs)

reset_head(n_outputs)

Replace the classification head for a new number of outputs.

This is called automatically by from_pretrained() when the user passes an n_outputs that differs from the saved config. Override in subclasses that need a model-specific head structure.

Parameters:

n_outputs (int) – New number of output classes.

Examples

>>> from braindecode.models import BENDR
>>> model = BENDR(n_chans=22, n_times=1000, n_outputs=4)
>>> model.reset_head(10)
>>> model.n_outputs
10

Added in version 1.4.

to_dense_prediction_model(axis=(2, 3))

Transform a sequential model with strides to a model that outputs.

dense predictions by removing the strides and instead inserting dilations. Modifies model in-place.

Parameters:

axis (int or (int,int)) – Axis to transform (in terms of intermediate output axes) can either be 2, 3, or (2,3).

Return type:

None

Notes

Does not yet work correctly for average pooling. Prior to version 0.1.7, there had been a bug that could move strides backwards one layer.

Examples using braindecode.models.EEGModuleMixin

Cleaning EEG Data with EEGPrep for Trialwise Decoding

Cleaning EEG Data with EEGPrep for Trialwise Decoding