API Reference¶
This is the reference for classes (CamelCase names) and functions
(underscore_case names) of Braindecode.
Classifier¶
braindecode.classifier:
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Classifier that does not assume softmax activation. |
Regressor¶
braindecode.regressor:
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Regressor that calls loss function directly. |
Models¶
braindecode.models:
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Shallow ConvNet model from [R9432a19f6121-2]. |
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Deep ConvNet model from [Rb8ef6c2733ce-1]. |
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EEGNet model from [Rcbe3da2652d4-EEGNet]. |
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EEGNet v4 model from [Rbd7837e27d7f-EEGNet4]. |
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Hybrid ConvNet model from [R4d1af390a17e-3]. |
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Residual Network for EEG. |
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Temporal Convolutional Network (TCN) as described in [Rcfca6ae7a220-1]. Code adapted from https://github.com/locuslab/TCN/blob/master/TCN/tcn.py Parameters ———- n_in_chans: int number of input EEG channels n_outputs: int number of outputs of the decoding task (for example number of classes in classification) n_filters: int number of output filters of each convolution n_blocks: int number of temporal blocks in the network kernel_size: int kernel size of the convolutions drop_prob: float dropout probability add_log_softmax: bool whether to add a log softmax layer References ———- .. [Rcfca6ae7a220-1] Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. |
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Sleep staging architecture from [R89163c5eab6a-1]. |
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Returns shape of neural network output for batch size equal 1. |
Training¶
braindecode.training:
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Compute Loss after averaging predictions across time. |
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Class to compute scores for trials from a model that predicts (super)crops. |
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Epoch Scoring class that recomputes predictions after the epoch on the training in validation mode. |
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Assigning window predictions to trials while removing duplicate predictions. |
Datasets¶
braindecode.datasets:
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Returns samples from an mne.io.Raw object along with a target. |
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A base class for concatenated datasets. |
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Returns windows from an mne.Epochs object along with a target. |
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A class for moabb datasets. |
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Temple University Hospital (TUH) EEG Corpus (www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml#c_tueg). |
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Temple University Hospital (TUH) Abnormal EEG Corpus. |
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Sleep Physionet dataset. |
Data Utils¶
braindecode.datautil:
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Create a BaseConcatDataset of WindowsDatasets from X and y to be used for decoding with skorch and braindecode, where X is a list of pre-cut trials and y are corresponding targets. |
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Create WindowsDatasets from mne.RawArrays |
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Create WindowsDatasets from mne.Epochs |
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Windower that creates sliding windows. |
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Create windows based on events in mne.Raw. |
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Perform exponential moving demeanining. |
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Perform exponential moving standardization. |
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Zscore normalize continuous or windowed data in-place. |
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Scale continuous or windowed data in-place |
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Applies multiple bandpass filters to the signals in raw. |
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Load a stored BaseConcatDataset of BaseDatasets or WindowsDatasets from files. |
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Apply preprocessors to a concat dataset. |
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Preprocessor for an MNE Raw or Epochs object. |
Samplers¶
braindecode.samplers:
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Base sampler simplifying sampling from recordings. |
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Sample examples for the relative positioning task from [R0467437a2408-1]. |
Utils¶
braindecode.util:
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Set seeds for python random module numpy.random and torch. |