torcheeg.datasets

DEAPDataset

class torcheeg.datasets.DEAPDataset(root_path: str = './data_preprocessed_python', chunk_size: int = 128, overlap: int = 0, channel_num: int = 32, baseline_num: int = 3, baseline_chunk_size: int = 128, online_transform: Union[None, Callable] = None, offline_transform: Union[None, Callable] = None, label_transform: Union[None, Callable] = None, io_path: str = './io/deap', num_worker: int = 1, verbose: bool = True)[source]

Bases: BaseDataset

A multimodal dataset for the analysis of human affective states. 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: Koelstra et al.
Year: 2012
Download URL: https://www.eecs.qmul.ac.uk/mmv/datasets/deap/download.html
Reference: Koelstra S, Muhl C, Soleymani M, et al. DEAP: A database for emotion analysis; using physiological signals[J]. IEEE transactions on affective computing, 2011, 3(1): 18-31.
Stimulus: 40 one-minute long excerpts from music videos.
Signals: Electroencephalogram (32 channels at 512Hz, downsampled to 128Hz), skinconductance level (SCL), respiration amplitude, skin temperature,electrocardiogram, blood volume by plethysmograph, electromyograms ofZygomaticus and Trapezius muscles (EMGs), electrooculogram (EOG), face video (for 22 participants).
Rating: Arousal, valence, like/dislike, dominance (all ona scale from 1 to 9), familiarity (on a scale from 1 to 5).

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

s01.dat
s02.dat
s03.dat
…
s32.dat

An example dataset for CNN-based methods:

dataset = DEAPDataset(io_path=f'./deap',
                      root_path='./data_preprocessed_python',
                      offline_transform=transforms.Compose([
                          transforms.BandDifferentialEntropy(),
                          transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT)
                      ]),
                      online_transform=transforms.ToTensor(),
                      label_transform=transforms.Compose([
                          transforms.Select('valence'),
                          transforms.Binary(5.0),
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[128, 9, 9]),
# coresponding baseline signal (torch.Tensor[128, 9, 9]),
# label (int)

Another example dataset for CNN-based methods:

dataset = DEAPDataset(io_path=f'./deap',
                      root_path='./data_preprocessed_python',
                      online_transform=transforms.Compose([
                          transforms.To2d(),
                          transforms.ToTensor()
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select(['valence', 'arousal']),
                          transforms.Binary(5.0),
                          transforms.BinariesToCategory()
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[1, 32, 128]),
# coresponding baseline signal (torch.Tensor[1, 32, 128]),
# label (int)

An example dataset for GNN-based methods:

dataset = DEAPDataset(io_path=f'./deap',
                      root_path='./data_preprocessed_python',
                      online_transform=transforms.Compose([
                          transforms.ToG(DEAP_ADJACENCY_MATRIX)
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select('arousal'),
                          transforms.Binary(5.0)
                      ]))
print(dataset[0])
# EEG signal (torch_geometric.data.Data),
# coresponding baseline signal (torch_geometric.data.Data),
# 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.

Parameters

root_path (str) – Downloaded data files in pickled python/numpy (unzipped data_preprocessed_python.zip) formats (default: './data_preprocessed_python')
chunk_size (int) – Number of data points included in each EEG chunk as training or test samples. (default: 128)
overlap (int) – The number of overlapping data points between different chunks when dividing EEG chunks. (default: 0)
channel_num (int) – Number of channels used, of which the first 32 channels are EEG signals. (default: 32)
baseline_num (int) – Number of baseline signal chunks used. (default: 3)
baseline_chunk_size (int) – Number of data points included in each baseline signal chunk. The baseline signal in the DEAP dataset has a total of 384 data points. (default: 128)
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. (default: /io/deap)
num_worker (str) – How many subprocesses to use for data processing. (default: 1)
verbose (bool) – Whether to display logs during processing, such as progress bars, etc. (default: True)

DREAMERDataset

class torcheeg.datasets.DREAMERDataset(mat_path: str = './DREAMER.mat', chunk_size: int = 128, overlap: int = 0, channel_num: int = 14, baseline_num: int = 61, baseline_chunk_size: int = 128, online_transform: Union[None, Callable] = None, offline_transform: Union[None, Callable] = None, label_transform: Union[None, Callable] = None, io_path: str = './io/dreamer', num_worker: int = 1, verbose: bool = True)[source]

Bases: BaseDataset

A multi-modal database consisting of electroencephalogram and electrocardiogram signals recorded during affect elicitation by means of audio-visual stimuli. 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: Katsigiannis et al.
Year: 2017
Download URL: https://zenodo.org/record/546113
Reference: Katsigiannis S, Ramzan N. DREAMER: A database for emotion recognition through EEG and ECG signals from wireless low-cost off-the-shelf devices[J]. IEEE journal of biomedical and health informatics, 2017, 22(1): 98-107.
Stimulus: 18 movie clips.
Signals: Electroencephalogram (14 channels at 128Hz), and electrocardiogram (2 channels at 256Hz) of 23 subjects.
Rating: Arousal, valence, like/dislike, dominance, familiarity (all ona scale from 1 to 5).

In order to use this dataset, the download file DREAMER.mat is required.

An example dataset for CNN-based methods:

dataset = DREAMERDataset(io_path=f'./dreamer',
                      mat_path='./DREAMER.mat',
                      offline_transform=transforms.Compose([
                          transforms.BandDifferentialEntropy(),
                          transforms.ToGrid(DREAMER_CHANNEL_LOCATION_DICT)
                      ]),
                      online_transform=transforms.ToTensor(),
                      label_transform=transforms.Compose([
                          transforms.Select('valence'),
                          transforms.Binary(3.0),
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[128, 9, 9]),
# coresponding baseline signal (torch.Tensor[128, 9, 9]),
# label (int)

Another example dataset for CNN-based methods:

dataset = DREAMERDataset(io_path=f'./dreamer',
                      mat_path='./DREAMER.mat',
                      online_transform=transforms.Compose([
                          transforms.To2d(),
                          transforms.ToTensor()
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select(['valence', 'arousal']),
                          transforms.Binary(3.0),
                          transforms.BinariesToCategory()
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[1, 14, 128]),
# coresponding baseline signal (torch.Tensor[1, 14, 128]),
# label (int)

An example dataset for GNN-based methods:

dataset = DREAMERDataset(io_path=f'./dreamer',
                      mat_path='./DREAMER.mat',
                      online_transform=transforms.Compose([
                          transforms.ToG(DREAMER_ADJACENCY_MATRIX)
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select('arousal'),
                          transforms.Binary(3.0)
                      ]))
print(dataset[0])
# EEG signal (torch_geometric.data.Data),
# coresponding baseline signal (torch_geometric.data.Data),
# 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.

Parameters

mat_path (str) – Downloaded data files in pickled matlab formats (default: './DREAMER.mat')
chunk_size (int) – Number of data points included in each EEG chunk as training or test samples. (default: 128)
overlap (int) – The number of overlapping data points between different chunks when dividing EEG chunks. (default: 0)
channel_num (int) – Number of channels used, of which the first 14 channels are EEG signals. (default: 14)
baseline_num (int) – Number of baseline signal chunks used. (default: 61)
baseline_chunk_size (int) – Number of data points included in each baseline signal chunk. The baseline signal in the DREAMER dataset has a total of 7808 data points. (default: 128)
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. (default: /io/dreamer)
num_worker (str) – How many subprocesses to use for data processing. (default: 1)
verbose (bool) – Whether to display logs during processing, such as progress bars, etc. (default: True)

adjacency_matrix = [[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]]

channel_location_dict = {'AF3': [1, 3], 'AF4': [1, 5], 'F3': [2, 2], 'F4': [2, 6], 'F7': [2, 0], 'F8': [2, 8], 'FC5': [3, 1], 'FC6': [3, 7], 'O1': [8, 3], 'O2': [8, 5], 'P7': [6, 0], 'P8': [6, 8], 'T7': [4, 0], 'T8': [4, 8]}

SEEDDataset

class torcheeg.datasets.SEEDDataset(root_path: str = './Preprocessed_EEG', chunk_size: int = 200, overlap: int = 0, channel_num: int = 62, online_transform: Union[None, Callable] = None, offline_transform: Union[None, Callable] = None, label_transform: Union[None, Callable] = None, io_path: str = './io/seed', num_worker: int = 1, verbose: bool = True)[source]

Bases: BaseDataset

The SJTU Emotion EEG Dataset (SEED), is a collection of EEG datasets provided by the BCMI laboratory, which is led by Prof. Bao-Liang Lu. 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: Zheng et al.
Year: 2015
Download URL: https://bcmi.sjtu.edu.cn/home/seed/index.html
Reference: Zheng W L, Lu B L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks[J]. IEEE Transactions on Autonomous Mental Development, 2015, 7(3): 162-175.
Stimulus: 15 four-minute long film clips from six Chinese movies.
Signals: Electroencephalogram (62 channels at 200Hz) of 15 subjects, and eye movement data of 12 subjects. Each subject conducts the experiment three times, with an interval of about one week, totally 15 people x 3 times = 45
Rating: positive (-1), negative (0), and neutral (1).

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

label.mat
readme.txt
10_20131130.mat
…
9_20140704.mat

An example dataset for CNN-based methods:

dataset = SEEDDataset(io_path=f'./seed',
                      root_path='./Preprocessed_EEG',
                      offline_transform=transforms.Compose([
                          transforms.BandDifferentialEntropy(),
                          transforms.ToGrid(SEED_CHANNEL_LOCATION_DICT)
                      ]),
                      online_transform=transforms.ToTensor(),
                      label_transform=transforms.Compose([
                          transforms.Select(['emotion']),
                          transforms.Lambda(x: x + 1)
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[200, 9, 9]),
# coresponding baseline signal (torch.Tensor[200, 9, 9]),
# label (int)

Another example dataset for CNN-based methods:

dataset = SEEDDataset(io_path=f'./seed',
                      root_path='./Preprocessed_EEG',
                      online_transform=transforms.Compose([
                          transforms.ToTensor(),
                          transforms.To2d()
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select(['emotion']),
                          transforms.Lambda(x: x + 1)
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[62, 200]),
# coresponding baseline signal (torch.Tensor[62, 200]),
# label (int)

An example dataset for GNN-based methods:

dataset = SEEDDataset(io_path=f'./seed',
                      root_path='./Preprocessed_EEG',
                      online_transform=transforms.Compose([
                          transforms.ToG(SEED_ADJACENCY_MATRIX)
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select(['emotion']),
                          transforms.Lambda(x: x + 1)
                      ]))
print(dataset[0])
# EEG signal (torch_geometric.data.Data),
# coresponding baseline signal (torch_geometric.data.Data),
# 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.

Parameters

root_path (str) – Downloaded data files in matlab (unzipped Preprocessed_EEG.zip) formats (default: './Preprocessed_EEG')
chunk_size (int) – Number of data points included in each EEG chunk as training or test samples. (default: 200)
overlap (int) – The number of overlapping data points between different chunks when dividing EEG chunks. (default: 0)
channel_num (int) – Number of channels used, of which the first 62 channels are EEG signals. (default: 62)
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. (default: /io/seed)
num_worker (str) – How many subprocesses to use for data processing. (default: 1)
verbose (bool) – Whether to display logs during processing, such as progress bars, etc. (default: True)

adjacency_matrix = [[0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 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0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0]]

channel_location_dict = {'AF3': [1, 3], 'AF4': [1, 5], 'C1': [4, 3], 'C2': [4, 5], 'C3': [4, 2], 'C4': [4, 6], 'C5': [4, 1], 'C6': [4, 7], 'CB1': [8, 2], 'CB2': [8, 6], 'CP1': [5, 3], 'CP2': [5, 5], 'CP3': [5, 2], 'CP4': [5, 6], 'CP5': [5, 1], 'CP6': [5, 7], 'CPZ': [5, 4], 'CZ': [4, 4], 'F1': [2, 3], 'F2': [2, 5], 'F3': [2, 2], 'F4': [2, 6], 'F5': [2, 1], 'F6': [2, 7], 'F7': [2, 0], 'F8': [2, 8], 'FC1': [3, 3], 'FC2': [3, 5], 'FC3': [3, 2], 'FC4': [3, 6], 'FC5': [3, 1], 'FC6': [3, 7], 'FCZ': [3, 4], 'FP1': [0, 3], 'FP2': [0, 5], 'FPZ': [0, 4], 'FT7': [3, 0], 'FT8': [3, 8], 'FZ': [2, 4], 'O1': [8, 3], 'O2': [8, 5], 'OZ': [8, 4], 'P1': [6, 3], 'P2': [6, 5], 'P3': [6, 2], 'P4': [6, 6], 'P5': [6, 1], 'P6': [6, 7], 'P7': [6, 0], 'P8': [6, 8], 'PO3': [7, 3], 'PO4': [7, 5], 'PO5': [7, 2], 'PO6': [7, 6], 'PO7': [7, 1], 'PO8': [7, 7], 'POZ': [7, 4], 'PZ': [6, 4], 'T7': [4, 0], 'T8': [4, 8], 'TP7': [5, 0], 'TP8': [5, 8]}

AMIGOSDataset

class torcheeg.datasets.AMIGOSDataset(root_path: str = './data_preprocessed', chunk_size: int = 128, overlap: int = 0, channel_num: int = 14, trial_num: int = 16, skipped_subjects: List[int] = [9, 12, 21, 22, 23, 24, 33], baseline_num: int = 5, baseline_chunk_size: int = 128, online_transform: Union[None, Callable] = None, offline_transform: Union[None, Callable] = None, label_transform: Union[None, Callable] = None, io_path: str = './io/amigos', num_worker: int = 1, verbose: bool = True)[source]

Bases: BaseDataset

A dataset for Multimodal research of affect, personality traits and mood on Individuals and GrOupS (AMIGOS). 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: Miranda-Correa et al.
Year: 2018
Download URL: http://www.eecs.qmul.ac.uk/mmv/datasets/amigos/download.html
Reference: Miranda-Correa J A, Abadi M K, Sebe N, et al. Amigos: A dataset for affect, personality and mood research on individuals and groups[J]. IEEE Transactions on Affective Computing, 2018, 12(2): 479-493.
Stimulus: 16 short affective video extracts and 4 long affective video extracts from movies.
Signals: Electroencephalogram (14 channels at 128Hz), electrocardiogram (2 channels at 60Hz) and galvanic skin response (1 channel at 60Hz) of 40 subjects. For the first 16 trials, 40 subjects watched a set of short affective video extracts. For the last 4 trials, 37 of the participants of the previous experiment watched a set of long affective video extracts.
Rating: arousal (1-9), valence (1-9), dominance (1-9), liking (1-9), familiarity (1-9), neutral (0, 1), disgust (0, 1),happiness (0, 1), surprise (0, 1), anger (0, 1), fear (0, 1), and sadness (0, 1).

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

Data_Preprocessed_P01.mat
Data_Preprocessed_P02.mat
Data_Preprocessed_P03.mat
…
Data_Preprocessed_P40.mat

An example dataset for CNN-based methods:

dataset = AMIGOSDataset(io_path=f'./amigos',
                        root_path='./data_preprocessed',
                        offline_transform=transforms.Compose([
                            transforms.BandDifferentialEntropy(),
                            transforms.ToGrid(AMIGOS_CHANNEL_LOCATION_DICT)
                        ]),
                        online_transform=transforms.ToTensor(),
                        label_transform=transforms.Compose([
                            transforms.Select('valence'),
                            transforms.Binary(5.0),
                        ]))
print(dataset[0])
# EEG signal (torch.Tensor[128, 9, 9]),
# coresponding baseline signal (torch.Tensor[128, 9, 9]),
# label (int)

Another example dataset for CNN-based methods:

dataset = AMIGOSDataset(io_path=f'./amigos',
                      root_path='./data_preprocessed',
                      online_transform=transforms.Compose([
                          transforms.To2d(),
                          transforms.ToTensor()
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select('valence'),
                          transforms.Binary(5.0),
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[14, 128]),
# coresponding baseline signal (torch.Tensor[14, 128]),
# label (int)

An example dataset for GNN-based methods:

dataset = AMIGOSDataset(io_path=f'./amigos',
                      root_path='./data_preprocessed',
                      online_transform=transforms.Compose([
                          transforms.ToG(AMIGOS_ADJACENCY_MATRIX)
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select('valence'),
                          transforms.Binary(5.0),
                      ]))
print(dataset[0])
# EEG signal (torch_geometric.data.Data),
# coresponding baseline signal (torch_geometric.data.Data),
# 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.

Parameters

root_path (str) – Downloaded data files in matlab (unzipped data_preprocessed.zip) formats (default: './data_preprocessed')
chunk_size (int) – Number of data points included in each EEG chunk as training or test samples. (default: 128)
overlap (int) – The number of overlapping data points between different chunks when dividing EEG chunks. (default: 0)
channel_num (int) – Number of channels used, of which the first 14 channels are EEG signals. (default: 14)
trial_num (int) – Number of trials used, of which the first 16 trials are conducted with short videos and the last 4 trials are conducted with long videos. If set to -1, all trials are used. (default: 16)
skipped_subjects (int) – The participant ID to be removed because there are some invalid data in the preprocessed version. (default: [9, 12, 21, 22, 23, 24, 33])
baseline_num (int) – Number of baseline signal chunks used. (default: 5)
baseline_chunk_size (int) – Number of data points included in each baseline signal chunk. The baseline signal in the AMIGOS dataset has a total of 640 data points. (default: 128)
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. (default: /io/amigos)
num_worker (str) – How many subprocesses to use for data processing. (default: 1)
verbose (bool) – Whether to display logs during processing, such as progress bars, etc. (default: True)

adjacency_matrix = [[0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]]

channel_location_dict = {'AF3': [1, 3], 'AF4': [1, 5], 'F3': [2, 2], 'F4': [2, 6], 'F7': [2, 0], 'F8': [2, 8], 'FC5': [3, 1], 'FC6': [3, 7], 'O1': [8, 3], 'O2': [8, 5], 'P7': [6, 0], 'P8': [6, 8], 'T7': [4, 0], 'T8': [4, 8]}

MAHNOBDataset

class torcheeg.datasets.MAHNOBDataset(root_path: str = './Sessions', chunk_size: int = 128, sampling_rate: int = 128, overlap: int = 0, channel_num: int = 32, baseline_num: int = 30, baseline_chunk_size: int = 128, trial_sample_num: int = 30, online_transform: Union[None, Callable] = None, offline_transform: Union[None, Callable] = None, label_transform: Union[None, Callable] = None, io_path: str = './io/mahnob', num_worker: int = 1, verbose: bool = True)[source]

Bases: BaseDataset

MAHNOB-HCI is a multimodal database recorded in response to affective stimuli with the goal of emotion recognition and implicit tagging research. 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: Soleymani et al.
Year: 2011
Download URL: https://mahnob-db.eu/hci-tagging/
Reference: Soleymani M, Lichtenauer J, Pun T, et al. A multimodal database for affect recognition and implicit tagging[J]. IEEE transactions on affective computing, 2011, 3(1): 42-55.
Stimulus: 20 videos from famous movies. Each video clip lasts 34-117 seconds (may not be an integer), in addition to 30 seconds before the beginning of the affective stimuli experience and another 30 seconds after the end.
Signals: Electroencephalogram (32 channels at 512Hz), peripheral physiological signals (ECG, GSR, Temp, Resp at 256 Hz), and eye movement signals (at 60Hz) of 30-5=25 subjects (3 subjects with missing data records and 2 subjects with incomplete data records).
Rating: Arousal, valence, control and predictability (all ona scale from 1 to 9).

In order to use this dataset, the download folder Sessions (Physiological files of emotion elicitation) is required, containing the following files:

1
- Part_1_N_Trial1_emotion.bdf
- session.xml
…
3810
- Part_30_S_Trial20_emotion.bdf
- session.xml

An example dataset for CNN-based methods:

dataset = MAHNOBDataset(io_path=f'./mahnob',
                      root_path='./Sessions',
                      offline_transform=transforms.Compose([
                          transforms.BandDifferentialEntropy(),
                          transforms.ToGrid(MAHNOB_CHANNEL_LOCATION_DICT)
                      ]),
                      online_transform=transforms.ToTensor(),
                      label_transform=transforms.Compose([
                          transforms.Select('feltVlnc'),
                          transforms.Binary(5.0),
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[128, 9, 9]),
# coresponding baseline signal (torch.Tensor[128, 9, 9]),
# label (int)

Another example dataset for CNN-based methods:

dataset = MAHNOBDataset(io_path=f'./mahnob',
                      root_path='./Sessions',
                      online_transform=transforms.Compose([
                          transforms.To2d(),
                          transforms.ToTensor()
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select(['feltVlnc', 'feltArsl']),
                          transforms.Binary(5.0),
                          transforms.BinariesToCategory()
                      ]))
print(dataset[0])
# EEG signal (torch.Tensor[1, 32, 128]),
# coresponding baseline signal (torch.Tensor[1, 32, 128]),
# label (int)

An example dataset for GNN-based methods:

dataset = MAHNOBDataset(io_path=f'./mahnob',
                      root_path='./Sessions',
                      online_transform=transforms.Compose([
                          transforms.ToG(MAHNOB_ADJACENCY_MATRIX)
                      ]),
                      label_transform=transforms.Compose([
                          transforms.Select('feltArsl'),
                          transforms.Binary(5.0)
                      ]))
print(dataset[0])
# EEG signal (torch_geometric.data.Data),
# coresponding baseline signal (torch_geometric.data.Data),
# 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.

Parameters

root_path (str) – Downloaded data files in bdf and xml (unzipped Sessions.zip) formats (default: './Sessions')
chunk_size (int) – Number of data points included in each EEG chunk as training or test samples. (default: 128)
sampling_rate (int) – The number of data points taken over a second. (default: 128)
overlap (int) – The number of overlapping data points between different chunks when dividing EEG chunks. (default: 0)
channel_num (int) – Number of channels used, of which the first 32 channels are EEG signals. (default: 32)
baseline_num (int) – Number of baseline signal chunks used. (default: 30)
baseline_chunk_size (int) – Number of data points included in each baseline signal chunk. The baseline signal in the MAHNOB dataset has a total of 512 (downsampled to sampling_rate) * 30 data points. (default: 128)
trial_sample_num (int) – Number of samples picked from each trial. If set to -1, all samples in trials are used. (default: 30)
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. (default: /io/mahnob)
num_worker (str) – How many subprocesses to use for data processing. (default: 1)
verbose (bool) – Whether to display logs during processing, such as progress bars, etc. (default: True)

adjacency_matrix = [[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 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0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]]

channel_location_dict = {'AF3': [1, 3], 'AF4': [1, 5], 'C3': [4, 2], 'C4': [4, 6], 'CP1': [5, 3], 'CP2': [5, 5], 'CP5': [5, 1], 'CP6': [5, 7], 'CZ': [4, 4], 'F3': [2, 2], 'F4': [2, 6], 'F7': [2, 0], 'F8': [2, 8], 'FC1': [3, 3], 'FC2': [3, 5], 'FC5': [3, 1], 'FC6': [3, 7], 'FP1': [0, 3], 'FP2': [0, 5], 'FZ': [2, 4], 'O1': [8, 3], 'O2': [8, 5], 'OZ': [8, 4], 'P3': [6, 2], 'P4': [6, 6], 'P7': [6, 0], 'P8': [6, 8], 'PO3': [7, 3], 'PO4': [7, 5], 'PZ': [6, 4], 'T7': [4, 0], 'T8': [4, 8]}