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
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  • Examples
  • View page source

Examples¶

Split Dataset Example

Split Dataset Example¶

Load and save dataset example

Load and save dataset example¶

MNE Dataset Example

MNE Dataset Example¶

Custom Dataset Example

Custom Dataset Example¶

Multiple discrete targets with the TUH EEG Corpus

Multiple discrete targets with the TUH EEG Corpus¶

Benchmarking preprocessing with parallelization and serialization

Benchmarking preprocessing with parallelization and serialization¶

MOABB Dataset Example

MOABB Dataset Example¶

Regression example on fake data

Regression example on fake data¶

Process a big data EEG resource (TUH EEG Corpus)

Process a big data EEG resource (TUH EEG Corpus)¶

Sleep staging on the Sleep Physionet dataset using U-Sleep network

Sleep staging on the Sleep Physionet dataset using U-Sleep network¶

Data Augmentation on BCIC IV 2a Dataset

Data Augmentation on BCIC IV 2a Dataset¶

Sleep staging on the Sleep Physionet dataset using Eldele2021

Sleep staging on the Sleep Physionet dataset using Eldele2021¶

Sleep staging on the Sleep Physionet dataset using Chambon2018 network

Sleep staging on the Sleep Physionet dataset using Chambon2018 network¶

Trialwise Decoding on BCIC IV 2a Dataset

Trialwise Decoding on BCIC IV 2a Dataset¶

Cropped Decoding on BCIC IV 2a Dataset

Cropped Decoding on BCIC IV 2a Dataset¶

Fingers flexion decoding on BCIC IV 4 ECoG Dataset

Fingers flexion decoding on BCIC IV 4 ECoG Dataset¶

Benchmarking eager and lazy loading

Benchmarking eager and lazy loading¶

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset¶

Self-supervised learning on EEG with relative positioning

Self-supervised learning on EEG with relative positioning¶

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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