CSVFolderDataset

class torcheeg.datasets.CSVFolderDataset(csv_path: str = './data.csv', read_fn: None | ~typing.Callable = <function default_read_fn>, online_transform: None | ~typing.Callable = None, offline_transform: None | ~typing.Callable = None, label_transform: None | ~typing.Callable = None, io_path: None | str = None, io_size: int = 1048576, io_mode: str = 'lmdb', num_worker: int = 0, verbose: bool = True, **kwargs)

Read meta information from CSV file and read EEG data from folder according to the meta information. The CSV file should contain the following columns:

• subject_id (Optional): The subject id of the EEG data. Commonly used in training and testing dataset split.

• label (Optional): The label of the EEG data. Commonly used in training and testing dataset split.

• file_path (Required): The path to the EEG data file.

# data.csv
# | subject_id | trial_id | label | file_path                 |
# | ---------- | -------  | ----- | ------------------------- |
# | sub1       | 0        | 0     | './data/label1/sub1.fif' |
# | sub1       | 1        | 1     | './data/label2/sub1.fif' |
# | sub1       | 2        | 2     | './data/label3/sub1.fif' |
# | sub2       | 0        | 0     | './data/label1/sub2.fif' |
# | sub2       | 1        | 1     | './data/label2/sub2.fif' |
# | sub2       | 2        | 2     | './data/label3/sub2.fif' |

from torcheeg.datasets import CSVFolderDataset
from torcheeg import transforms

def default_read_fn(file_path, **kwargs):
    # Load EEG file
    raw = mne.io.read_raw(file_path)
    # Convert raw to epochs
    epochs = mne.make_fixed_length_epochs(raw, duration=1)
    # Return EEG data
    return epochs

dataset = CSVFolderDataset(csv_path='./data.csv',
                           read_fn=default_read_fn,
                           online_transform=transforms.ToTensor(),
                           label_transform=transforms.Select('label'),
                           num_worker=4)

Parameters:

• csv_path (str) – The path to the CSV file.

• read_fn (Callable) – Method for reading files in a folder. By default, this class provides methods for reading files using mne.io.read_raw. At the same time, we allow users to pass in custom file reading methods. The first input parameter of whose is file_path, and other parameters are additional parameters passed in when the class is initialized. For example, you can pass chunk_size=32 to FolderDataset, then chunk_size will be received here.

• 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)

• 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)