MNEDataset¶

class torcheeg.datasets.MNEDataset(epochs_list: List[Epochs], metadata_list: List[Dict], chunk_size: int = -1, overlap: int = 0, num_channel: int = -1, online_transform: Union[None, Callable] = None, offline_transform: Union[None, Callable] = None, label_transform: Union[None, Callable] = None, before_trial: Union[None, Callable] = None, after_trial: Union[None, Callable] = None, io_path: str = './io/mne', num_worker: int = 0, verbose: bool = True, cache_size: int = 68719476736)[source]¶

A generic EEG analysis dataset that allows creating datasets from mne.Epochs, and caches the generated results in a unified input and output format (IO). It is generally used to support custom datasets or datasets not yet provided by TorchEEG.

MNEDataset allows a list of mne.Epochs and the corresponding description dictionary as input, and divides the signals in mne.Epochs into several segments according to the configuration information provided by the user. These segments will be annotated by the description dictionary elements and the event type annotated in mne.Epochs. Here is an example case shows the use of MNEDataset:

# subject index and run index of mne.Epochs
metadata_list = [{
    'subject': 1,
    'run': 3
}, {
    'subject': 1,
    'run': 7
}, {
    'subject': 1,
    'run': 11
}]

epochs_list = []
for metadata in metadata_list:
    physionet_path = mne.datasets.eegbci.load_data(metadata['subject'],
                                                metadata['run'],
                                                update_path=False)[0]

    raw = mne.io.read_raw_edf(physionet_path, preload=True, stim_channel='auto')
    events, _ = mne.events_from_annotations(raw)
    picks = mne.pick_types(raw.info,
                        meg=False,
                        eeg=True,
                        stim=False,
                        eog=False,
                        exclude='bads')
    # init Epochs with raw EEG signals and corresponding event annotations
    epochs_list.append(mne.Epochs(raw, events, picks=picks))

# split into chunks of 160 data points (1s)
dataset = MNEDataset(epochs_list=epochs_list,
                    metadata_list=metadata_list,
                    chunk_size=160,
                    overlap=0,
                    num_channel=60,
                    io_path=io_path,
                    offline_transform=transforms.Compose(
                        [transforms.BandDifferentialEntropy()]),
                    online_transform=transforms.ToTensor(),
                    label_transform=transforms.Compose([
                        transforms.Select('event')
                    ]),
                    num_worker=2)
print(dataset[0])
# EEG signal (torch.Tensor[60, 4]),
# coresponding baseline signal (torch.Tensor[60, 4]),
# label (int)

In particular, TorchEEG utilizes the producer-consumer model to allow multi-process data preprocessing. If your data preprocessing is time consuming, consider increasing num_worker for higher speedup. If running under Windows, please use the proper idiom in the main module:

if __name__ == '__main__':
    # subject index and run index of mne.Epochs
    metadata_list = [{
        'subject': 1,
        'run': 3
    }, {
        'subject': 1,
        'run': 7
    }, {
        'subject': 1,
        'run': 11
    }]

    epochs_list = []
    for metadata in metadata_list:
        physionet_path = mne.datasets.eegbci.load_data(metadata['subject'],
                                                    metadata['run'],
                                                    update_path=False)[0]

        raw = mne.io.read_raw_edf(physionet_path, preload=True, stim_channel='auto')
        events, _ = mne.events_from_annotations(raw)
        picks = mne.pick_types(raw.info,
                            meg=False,
                            eeg=True,
                            stim=False,
                            eog=False,
                            exclude='bads')
        # init Epochs with raw EEG signals and corresponding event annotations
        epochs_list.append(mne.Epochs(raw, events, picks=picks))

    # split into chunks of 160 data points (1s)
    dataset = MNEDataset(epochs_list=epochs_list,
                        metadata_list=metadata_list,
                        chunk_size=160,
                        overlap=0,
                        num_channel=60,
                        io_path=io_path,
                        offline_transform=transforms.Compose(
                            [transforms.BandDifferentialEntropy()]),
                        online_transform=transforms.ToTensor(),
                        label_transform=transforms.Compose([
                            transforms.Select('event')
                        ]),
                        num_worker=2)
    print(dataset[0])
    # EEG signal (torch.Tensor[60, 4]),
    # coresponding baseline signal (torch.Tensor[60, 4]),
    # label (int)

Parameters

epochs_list (list) – A list of mne.Epochs. MNEDataset will divide the signals in mne.Epochs into several segments according to the chunk_size and overlap information provided by the user. The divided segments will be transformed and cached in a unified input and output format (IO) for accessing.
metadata_list (list) – A list of dictionaries of the same length as epochs_list. Each of these dictionaries is annotated with meta-information about mne.Epochs, such as subject index, experimental dates, etc. These annotated meta-information will be added to the element corresponding to mne.Epochs for use as labels for the sample.
chunk_size (int) – Number of data points included in each EEG chunk as training or test samples. If set to -1, the EEG signal of a trial is used as a sample of a chunk. If set to -1, the EEG signal is not segmented, and the length of the chunk is the length of the event. (default: -1)
overlap (int) – The number of overlapping data points between different chunks when dividing EEG chunks. (default: 0)
num_channel (int) – Number of channels used. If set to -1, all electrodes are used (default: -1)
online_transform (Callable, optional) – The transformation of the EEG signals and baseline EEG signals. The input is a np.ndarray, and the ouput is used as the first and second value of each element in the dataset. (default: None)
offline_transform (Callable, optional) – The usage is the same as online_transform, but executed before generating IO intermediate results. (default: None)
label_transform (Callable, optional) – The transformation of the label. The input is an information dictionary, and the ouput is used as the third value of each element in the dataset. (default: None)
before_trial (Callable, optional) – The hook performed on the trial to which the sample belongs. It is performed before the offline transformation and thus typically used to implement context-dependent sample transformations, such as moving averages, etc. The input and output of this hook function should be a mne.Epoch.
after_trial (Callable, optional) – The hook performed on the trial to which the sample belongs. It is performed after the offline transformation and thus typically used to implement context-dependent sample transformations, such as moving averages, etc. The input and output of this hook function should be a sequence of dictionaries representing a sequence of EEG samples. Each dictionary contains two key-value pairs, indexed by eeg (the EEG signal matrix) and key (the index in the database) respectively
io_path (str) – The path to generated unified data IO, cached as an intermediate result. (default: /io/deap)
num_worker (str) – How many subprocesses to use for data processing. (default: 0)
verbose (bool) – Whether to display logs during processing, such as progress bars, etc. (default: True)
verbose – Whether to display logs during processing, such as progress bars, etc. (default: True)
cache_size (int) – Maximum size database may grow to; used to size the memory mapping. If database grows larger than map_size, an exception will be raised and the user must close and reopen. (default: 64 * 1024 * 1024 * 1024)

MNEDataset¶

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