SanDiegoSSVEPDataset¶

class torcheeg.datasets.SanDiegoSSVEPDataset(root_path='./archive', chunk_size: int = 256, 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]¶

San Diego Square Joint Frequnecy-Phase Modulation SSVEP Dataset: lightweight dataset for studying SSVEP. For more information, please refer to the details below.

Author: Masaki Nakanishi et al.
Year: 2015
Download URL: https://www.kaggle.com/datasets/lzyuuu/ssvep-sandiego
Reference: Masaki Nakanishi, Yijun Wang, Yu-Te Wang and Tzyy-Ping Jung,”A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials,” PLoS One, vol.10, no.10, e140703, 2015.
Stimulus: 12 different frequencies and phases of visual stimuli.
Signals: Electroencephalogram (8 channels at 256Hz). Training and testing sets have been divided for each participant (totally 10 participants) in original datasets .
label: The order of the stimulus frequencies in the EEG data is [9.25, 11.25, 13.25, 9.75, 11.75, 13.75, 10.25, 12.25, 14.25, 10.75, 12.75, 14.75] Hz which are labeled to range(0,12).

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

archive/
├── S01testEEG.mat
├── S01trainEEG.mat
├── S02testEEG.mat
├── ...
├── S010testEEG.mat
└── S010trainEEG.mat

An example:

from torcheeg.transforms import Select,BandSignal
dataset = SanDiegoSSVEPDataset(root_path=r'./archive',
                        chunk_size=512,  #2 second
                        io_path= r'./tmp_out/sandiego',
                        offline_transform=BandSignal(sampling_rate=256,band_dict={'frequency_range':[8,16]}),
                        label_transform=Select('label')
)
print(dataset[0][0].shape) #EEG shape(1,8,512)
print(dataset[0][1])  # label (int)

Parameters:

root_path (str) – Downloaded data files (unzipped) dir. (default: './archive')
chunk_size (int) – Number of data points included in each EEG chunk as training or test samples. (default: 256)
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)

SanDiegoSSVEPDataset¶

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