BCICIV2aDataset¶

class torcheeg.datasets.BCICIV2aDataset(root_path: str = './BCICIV_2a_mat', offset: int = 0, chunk_size: int = 1750, overlap: int = 0, num_channel: int = 22, skip_trial_with_artifacts: bool = False, online_transform: None | Callable = None, offline_transform: None | Callable = None, label_transform: None | Callable = None, before_trial: None | Callable = None, after_trial: None | Callable = None, after_session: None | Callable = None, after_subject: None | Callable = None, io_path: None | str = None, io_size: int = 1048576, io_mode: str = 'lmdb', num_worker: int = 0, verbose: bool = True)[source][source]¶

A dataset for motor imagery, BCI Competition 2008 Graz data set A (BCICIV_2a). This class generates training samples and test samples according to the given parameters, and caches the generated results in a unified input and output format (IO). The relevant information of the dataset is as follows:

Author: Tangermann et al.
Year: 2012
Download URL: http://bnci-horizon-2020.eu/database/data-sets
Reference: Tangermann M, Müller K R, Aertsen A, et al. Review of the BCI competition IV[J]. Frontiers in neuroscience, 2012: 55.
Signals: Electroencephalogram (22 channels at 250Hz) and electrocardiogram (3 channels at 250Hz) of 9 subjects. Two sessions on different days were recorded for each subject. Each session is comprised of 6 runs separated by short breaks. One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session. For more detail, please refer to https://www.bbci.de/competition/iv/desc_2a.pdf
Category: The imagination of movement of the left hand (class 1), right hand (class 2), both feet (class 3), and tongue (class 4).

In order to use this dataset, the download folder BCICIV_2a_mat is required, containing the following files:

An example dataset for CNN-based methods:

from torcheeg.datasets import BCICIV2aDataset
from torcheeg import transforms

dataset = BCICIV2aDataset(root_path='./BCICIV_2a_mat',
                          online_transform=transforms.Compose([
                              transforms.To2d(),
                              transforms.ToTensor()
                          ]),
                          label_transform=transforms.Compose([
                              transforms.Select('label'),
                              transforms.Lambda(lambda x: x - 1)
                          ]))
print(dataset[0])
# EEG signal (torch.Tensor[1, 22, 1750])
# label (int)

Parameters:

root_path (str) – Downloaded data files in matlab (unzipped BCICIV_2a_mat.zip) formats (default: './BCICIV_2a_mat')
offset (int) – The number of data points to be discarded at the beginning of each trial. (default: 0)
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. (default: 7 * 250)
overlap (int) – The number of overlapping data points between different chunks when dividing EEG chunks. (default: 0)
num_channel (int) – Number of channels used, of which the first 14 channels are EEG signals. (default: 22)
skip_trial_with_artifacts (bool) – Whether to skip trials with artifacts. (default: False)
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 of this hook function is a 2D EEG signal with shape (number of electrodes, number of data points), whose ideal output shape is also (number of electrodes, number of data points).
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. If set to None, a random path will be generated. (default: None)
io_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: 1048576)
io_mode (str) – Storage mode of EEG signal. When io_mode is set to lmdb, TorchEEG provides an efficient database (LMDB) for storing EEG signals. LMDB may not perform well on limited operating systems, where a file system based EEG signal storage is also provided. When io_mode is set to pickle, pickle-based persistence files are used. When io_mode is set to memory, memory are used. (default: lmdb)
num_worker (int) – Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. (default: 0)
verbose (bool) – Whether to display logs during processing, such as progress bars, etc. (default: True)

BCICIV2aDataset¶

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