Welcome to TorchEEG’s documentation!¶
TorchEEG is a library built on PyTorch for EEG signal analysis. TorchEEG aims to provide a plug-and-play EEG analysis tool, so that researchers can quickly reproduce EEG analysis work and start new EEG analysis research without paying attention to technical details unrelated to the research focus.
TorchEEG specifies a unified data input-output format (IO) and implement commonly used EEG databases, allowing users to quickly access benchmark datasets and define new custom datasets. The datasets that have been defined so far include emotion recognition and so on. According to papers published in the field of EEG analysis, TorchEEG provides data preprocessing methods commonly used for EEG signals, and provides plug-and-play API for both offline and online pre-proocessing. Offline processing allow users to process once and use any times, speeding up the training process. Online processing allows users to save time when creating new data processing methods. TorchEEG also provides deep learning models following published papers for EEG analysis, including convolutional neural networks, graph convolutional neural networks, and Transformers.
User Documentation:
Tutorials:
Package Reference:
- torcheeg.datasets
- torcheeg.io
- torcheeg.model_selection
- KFold
- KFoldPerSubject
- KFoldCrossSubject
- KFoldGroupbyTrial
- KFoldPerSubjectGroupbyTrial
- KFoldCrossTrial
- KFoldPerSubjectCrossTrial
- LeaveOneSubjectOut
- Subcategory
- train_test_split
- train_test_split_groupby_trial
- train_test_split_per_subject_groupby_trial
- train_test_split_cross_trial
- train_test_split_per_subject_cross_trial
- torcheeg.models
- torcheeg.transforms
- torcheeg.trainers
- torcheeg.utils
