Braindecode
0.6
  • Basic trialwise decoding
    • Loading and preprocessing the dataset
      • Loading
      • Preprocessing
      • Cut Compute Windows
      • Split dataset into train and valid
    • Create model
    • Training
    • Plot Results
    • Plot Confusion Matrix
  • More data-efficient "cropped decoding"
    • Loading and preprocessing the dataset
    • Create model and compute windowing parameters
    • Cut the data into windows
    • Split the dataset
    • Training
    • Plot Results
    • Plot Confusion Matrix
  • Your own datasets through MNE
  • Your own datasets through Numpy
  • Examples
    • Split Dataset Example
    • Load and save dataset example
    • MNE Dataset Example
    • Custom Dataset Example
    • Multiple discrete targets with the TUH EEG Corpus
    • Benchmarking preprocessing with parallelization and serialization
    • MOABB Dataset Example
    • Regression example on fake data
    • Process a big data EEG resource (TUH EEG Corpus)
    • Sleep staging on the Sleep Physionet dataset using U-Sleep network
      • References
      • Loading and preprocessing the dataset
        • Loading
        • Preprocessing
        • Extract windows
        • Split dataset into train and valid
      • Create sequence samplers
      • Create model
      • Training
      • Plot results
    • Data Augmentation on BCIC IV 2a Dataset
      • Loading and preprocessing the dataset
        • Loading
        • Preprocessing
        • Extracting windows
        • Split dataset into train and valid
      • Defining a Transform
        • Manipulating one session and visualizing the transformed data
      • Training a model with data augmentation
        • Create model
        • Create an EEGClassifier with the desired augmentation
        • Manually composing Transforms
        • Setting the data augmentation at the Dataset level
    • Sleep staging on the Sleep Physionet dataset using Eldele2021
      • References
      • Loading and preprocessing the dataset
        • Loading
        • Preprocessing
        • Extract windows
        • Window preprocessing
        • Split dataset into train and valid
      • Create sequence samplers
      • Create model
      • Training
      • Plot results
    • Sleep staging on the Sleep Physionet dataset using Chambon2018 network
      • References
      • Loading and preprocessing the dataset
        • Loading
        • Preprocessing
        • Extract windows
        • Window preprocessing
        • Split dataset into train and valid
      • Create sequence samplers
      • Create model
      • Training
      • Plot results
    • Trialwise Decoding on BCIC IV 2a Dataset
      • Loading and preprocessing the dataset
        • Loading
        • Preprocessing
        • Cut Compute Windows
        • Split dataset into train and valid
      • Create model
      • Training
      • Plot Results
      • Plot Confusion Matrix
    • Cropped Decoding on BCIC IV 2a Dataset
      • Loading and preprocessing the dataset
      • Create model and compute windowing parameters
      • Cut the data into windows
      • Split the dataset
      • Training
      • Plot Results
      • Plot Confusion Matrix
    • Fingers flexion decoding on BCIC IV 4 ECoG Dataset
      • Loading and preparing the dataset
        • Loading
        • Preprocessing
        • Cut Compute Windows
        • Split dataset into train, valid, and test
      • Create model
      • Training
      • Plot Results
    • Benchmarking eager and lazy loading
    • Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset
      • Loading and preparing the dataset
        • Loading
        • Split dataset into train and test
        • Preprocessing
      • Create model
        • Cut Compute Windows
      • Training
      • Plot Results
    • Self-supervised learning on EEG with relative positioning
      • Loading and preprocessing the dataset
        • Loading the raw recordings
        • Preprocessing
        • Extracting windows
        • Preprocessing windows
        • Splitting dataset into train, valid and test sets
        • Creating samplers
      • Creating the model
      • Training
      • Visualizing the results
        • Inspecting pretext task performance
        • Using the learned representation for sleep staging
      • Conclusion
      • References
  • API Reference
    • Classifier
      • braindecode.classifier.EEGClassifier
    • Regressor
      • braindecode.regressor.EEGRegressor
    • Models
      • braindecode.models.ShallowFBCSPNet
        • Examples using braindecode.models.ShallowFBCSPNet
      • braindecode.models.Deep4Net
        • Examples using braindecode.models.Deep4Net
      • braindecode.models.EEGNetv1
      • braindecode.models.EEGNetv4
      • braindecode.models.HybridNet
      • braindecode.models.EEGResNet
      • braindecode.models.TCN
      • braindecode.models.SleepStagerChambon2018
        • Examples using braindecode.models.SleepStagerChambon2018
      • braindecode.models.SleepStagerBlanco2020
      • braindecode.models.SleepStagerEldele2021
        • Examples using braindecode.models.SleepStagerEldele2021
      • braindecode.models.USleep
        • Examples using braindecode.models.USleep
      • braindecode.models.TIDNet
      • braindecode.models.get_output_shape
        • Examples using braindecode.models.get_output_shape
      • braindecode.models.TimeDistributed
        • Examples using braindecode.models.TimeDistributed
    • Training
      • braindecode.training.CroppedLoss
        • Examples using braindecode.training.CroppedLoss
      • braindecode.training.TimeSeriesLoss
        • Examples using braindecode.training.TimeSeriesLoss
      • braindecode.training.CroppedTrialEpochScoring
        • Examples using braindecode.training.CroppedTrialEpochScoring
      • braindecode.training.CroppedTimeSeriesEpochScoring
        • Examples using braindecode.training.CroppedTimeSeriesEpochScoring
      • braindecode.training.PostEpochTrainScoring
      • braindecode.training.mixup_criterion
      • braindecode.training.trial_preds_from_window_preds
      • braindecode.training.predict_trials
    • Datasets
      • braindecode.datasets.BaseDataset
        • Examples using braindecode.datasets.BaseDataset
      • braindecode.datasets.BaseConcatDataset
        • Examples using braindecode.datasets.BaseConcatDataset
      • braindecode.datasets.WindowsDataset
      • braindecode.datasets.MOABBDataset
        • Examples using braindecode.datasets.MOABBDataset
      • braindecode.datasets.HGD
      • braindecode.datasets.BNCI2014001
      • braindecode.datasets.TUH
        • Examples using braindecode.datasets.TUH
      • braindecode.datasets.TUHAbnormal
        • Examples using braindecode.datasets.TUHAbnormal
      • braindecode.datasets.SleepPhysionet
        • Examples using braindecode.datasets.SleepPhysionet
      • braindecode.datasets.BCICompetitionIVDataset4
        • Examples using braindecode.datasets.BCICompetitionIVDataset4
      • braindecode.datasets.create_from_X_y
        • Examples using braindecode.datasets.create_from_X_y
      • braindecode.datasets.create_from_mne_raw
        • Examples using braindecode.datasets.create_from_mne_raw
      • braindecode.datasets.create_from_mne_epochs
        • Examples using braindecode.datasets.create_from_mne_epochs
    • Preprocessing
      • braindecode.preprocessing.create_windows_from_events
        • Examples using braindecode.preprocessing.create_windows_from_events
      • braindecode.preprocessing.create_fixed_length_windows
        • Examples using braindecode.preprocessing.create_fixed_length_windows
      • braindecode.preprocessing.create_windows_from_target_channels
        • Examples using braindecode.preprocessing.create_windows_from_target_channels
      • braindecode.preprocessing.exponential_moving_demean
      • braindecode.preprocessing.exponential_moving_standardize
        • Examples using braindecode.preprocessing.exponential_moving_standardize
      • braindecode.preprocessing.zscore
      • braindecode.preprocessing.scale
        • Examples using braindecode.preprocessing.scale
      • braindecode.preprocessing.filterbank
      • braindecode.preprocessing.preprocess
        • Examples using braindecode.preprocessing.preprocess
      • braindecode.preprocessing.Preprocessor
        • Examples using braindecode.preprocessing.Preprocessor
    • Data Utils
      • braindecode.datautil.save_concat_dataset
      • braindecode.datautil.load_concat_dataset
        • Examples using braindecode.datautil.load_concat_dataset
    • Samplers
      • braindecode.samplers.RecordingSampler
        • Examples using braindecode.samplers.RecordingSampler
      • braindecode.samplers.SequenceSampler
        • Examples using braindecode.samplers.SequenceSampler
      • braindecode.samplers.RelativePositioningSampler
        • Examples using braindecode.samplers.RelativePositioningSampler
      • braindecode.samplers.BalancedSequenceSampler
    • Augmentation
      • braindecode.augmentation.Transform
        • Examples using braindecode.augmentation.Transform
      • braindecode.augmentation.IdentityTransform
      • braindecode.augmentation.Compose
        • Examples using braindecode.augmentation.Compose
      • braindecode.augmentation.AugmentedDataLoader
        • Examples using braindecode.augmentation.AugmentedDataLoader
      • braindecode.augmentation.TimeReverse
      • braindecode.augmentation.SignFlip
        • Examples using braindecode.augmentation.SignFlip
      • braindecode.augmentation.FTSurrogate
      • braindecode.augmentation.ChannelsShuffle
      • braindecode.augmentation.ChannelsDropout
      • braindecode.augmentation.GaussianNoise
      • braindecode.augmentation.ChannelsSymmetry
      • braindecode.augmentation.SmoothTimeMask
      • braindecode.augmentation.BandstopFilter
      • braindecode.augmentation.FrequencyShift
        • Examples using braindecode.augmentation.FrequencyShift
      • braindecode.augmentation.SensorsRotation
      • braindecode.augmentation.SensorsZRotation
      • braindecode.augmentation.SensorsYRotation
      • braindecode.augmentation.SensorsXRotation
      • braindecode.augmentation.Mixup
      • braindecode.augmentation.functional.identity
      • braindecode.augmentation.functional.time_reverse
      • braindecode.augmentation.functional.sign_flip
      • braindecode.augmentation.functional.ft_surrogate
      • braindecode.augmentation.functional.channels_dropout
      • braindecode.augmentation.functional.channels_shuffle
      • braindecode.augmentation.functional.channels_permute
      • braindecode.augmentation.functional.gaussian_noise
      • braindecode.augmentation.functional.smooth_time_mask
      • braindecode.augmentation.functional.bandstop_filter
      • braindecode.augmentation.functional.frequency_shift
      • braindecode.augmentation.functional.sensors_rotation
      • braindecode.augmentation.functional.mixup
    • Utils
      • braindecode.util.set_random_seeds
        • Examples using braindecode.util.set_random_seeds
    • Visualization
      • braindecode.visualization.compute_amplitude_gradients
      • braindecode.visualization.plot_confusion_matrix
        • Examples using braindecode.visualization.plot_confusion_matrix
  • What’s new
    • Version 0.6 (2021-12-06)
      • Enhancements
      • Bugs
      • API changes
    • Version 0.5.1 (2021-07-14)
      • Enhancements
      • Bugs
      • API changes
      • Authors
Braindecode
  • »
  • Search

© Copyright 2018-2021, Braindecode developers.

Built with Sphinx using a theme provided by Read the Docs.