Source code for torcheeg.datasets.module.emotion_recognition.deap

import os
import pickle as pkl
from typing import Any, Callable, Dict, Tuple, Union

from ..base_dataset import BaseDataset


class DEAPDataset(BaseDataset):
    r'''
    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 :obj:`data_preprocessed_python` is required, containing the following files:
    
    - s01.dat
    - s02.dat
    - s03.dat
    - ...
    - s32.dat

    An example dataset for CNN-based methods:

    .. code-block:: python
    
        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[4, 9, 9]),
        # coresponding baseline signal (torch.Tensor[4, 9, 9]),
        # label (int)

    Another example dataset for CNN-based methods:

    .. code-block:: python

        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:

    .. code-block:: python
    
        dataset = DEAPDataset(io_path=f'./deap',
                              root_path='./data_preprocessed_python',
                              online_transform=transforms.Compose([
                                  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 :obj:`num_worker` for higher speedup. If running under Windows, please use the proper idiom in the main module:

    .. code-block:: python
    
        if __name__ == '__main__':
            dataset = DEAPDataset(io_path=f'./deap',
                              root_path='./data_preprocessed_python',
                              online_transform=transforms.Compose([
                                  ToG(DEAP_ADJACENCY_MATRIX)
                              ]),
                              label_transform=transforms.Compose([
                                  transforms.Select('arousal'),
                                  transforms.Binary(5.0)
                              ]),
                              num_worker=4)
            print(dataset[0])
            # EEG signal (torch_geometric.data.Data),
            # coresponding baseline signal (torch_geometric.data.Data),
            # label (int)

    Args:
        root_path (str): Downloaded data files in pickled python/numpy (unzipped data_preprocessed_python.zip) formats (default: :obj:`'./data_preprocessed_python'`)
        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: :obj:`128`)
        overlap (int): The number of overlapping data points between different chunks when dividing EEG chunks. (default: :obj:`0`)
        num_channel (int): Number of channels used, of which the first 32 channels are EEG signals. (default: :obj:`32`)
        num_baseline (int): Number of baseline signal chunks used. (default: :obj:`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: :obj:`128`)
        online_transform (Callable, optional): The transformation of the EEG signals and baseline EEG signals. The input is a :obj:`np.ndarray`, and the ouput is used as the first and second value of each element in the dataset. (default: :obj:`None`)
        offline_transform (Callable, optional): The usage is the same as :obj:`online_transform`, but executed before generating IO intermediate results. (default: :obj:`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: :obj:`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 :obj:`eeg` (the EEG signal matrix) and :obj:`key` (the index in the database) respectively.
        io_path (str): The path to generated unified data IO, cached as an intermediate result. (default: :obj:`./io/deap`)
        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: :obj:`10485760`)
        io_mode (str): Storage mode of EEG signal. When io_mode is set to :obj:`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 :obj:`pickle`, pickle-based persistence files are used. (default: :obj:`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: :obj:`0`)
        verbose (bool): Whether to display logs during processing, such as progress bars, etc. (default: :obj:`True`)
        in_memory (bool): Whether to load the entire dataset into memory. If :obj:`in_memory` is set to True, then the first time an EEG sample is read, the entire dataset is loaded into memory for subsequent retrieval. Otherwise, the dataset is stored on disk to avoid the out-of-memory problem. (default: :obj:`False`)
    '''
    def __init__(self,
                 root_path: str = './data_preprocessed_python',
                 chunk_size: int = 128,
                 overlap: int = 0,
                 num_channel: int = 32,
                 num_baseline: 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,
                 before_trial: Union[None, Callable] = None,
                 after_trial: Union[Callable, None] = None,
                 after_session: Union[Callable, None] = None,
                 after_subject: Union[Callable, None] = None,
                 io_path: str = './io/deap',
                 io_size: int = 10485760,
                 io_mode: str = 'lmdb',
                 num_worker: int = 0,
                 verbose: bool = True,
                 in_memory: bool = False):
        # pass all arguments to super class
        params = {
            'root_path': root_path,
            'chunk_size': chunk_size,
            'overlap': overlap,
            'num_channel': num_channel,
            'num_baseline': num_baseline,
            'baseline_chunk_size': baseline_chunk_size,
            'online_transform': online_transform,
            'offline_transform': offline_transform,
            'label_transform': label_transform,
            'before_trial': before_trial,
            'after_trial': after_trial,
            'after_session': after_session,
            'after_subject': after_subject,
            'io_path': io_path,
            'io_size': io_size,
            'io_mode': io_mode,
            'num_worker': num_worker,
            'verbose': verbose,
            'in_memory': in_memory
        }
        super().__init__(**params)
        # save all arguments to __dict__
        self.__dict__.update(params)

    @staticmethod
    def process_record(file: Any = None,
                   root_path: str = './data_preprocessed_python',
                   chunk_size: int = 128,
                   overlap: int = 0,
                   num_channel: int = 32,
                   num_baseline: int = 3,
                   baseline_chunk_size: int = 128,
                   before_trial: Union[None, Callable] = None,
                   after_trial: Union[Callable, None] = None,
                   offline_transform: Union[None, Callable] = None,
                   **kwargs):
        file_name = file  # an element from file name list

        # derive the given arguments (kwargs)
        with open(os.path.join(root_path, file_name), 'rb') as f:
            pkl_data = pkl.load(f, encoding='iso-8859-1')

        samples = pkl_data['data']  # trial(40), channel(32), timestep(63*128)
        labels = pkl_data['labels']
        subject_id = file_name

        write_pointer = 0
        
        for trial_id in range(len(samples)):

            # extract baseline signals
            trial_samples = samples[
                trial_id, :num_channel]  # channel(32), timestep(63*128)
            if before_trial:
                trial_samples = before_trial(trial_samples)

            trial_baseline_sample = trial_samples[:, :baseline_chunk_size *
                                                  num_baseline]  # channel(32), timestep(3*128)
            trial_baseline_sample = trial_baseline_sample.reshape(
                num_channel, num_baseline,
                baseline_chunk_size).mean(axis=1)  # channel(32), timestep(128)

            # record the common meta info
            trial_meta_info = {'subject_id': subject_id, 'trial_id': trial_id}
            trial_rating = labels[trial_id]

            for label_index, label_name in enumerate(
                ['valence', 'arousal', 'dominance', 'liking']):
                trial_meta_info[label_name] = trial_rating[label_index]

            start_at = baseline_chunk_size * num_baseline
            if chunk_size <= 0:
                dynamic_chunk_size = trial_samples.shape[1] - start_at
            else:
                dynamic_chunk_size = chunk_size

            # chunk with chunk size
            end_at = start_at + dynamic_chunk_size
            # calculate moving step
            step = dynamic_chunk_size - overlap

            while end_at <= trial_samples.shape[1]:
                clip_sample = trial_samples[:, start_at:end_at]

                t_eeg = clip_sample
                t_baseline = trial_baseline_sample

                if not offline_transform is None:
                    t = offline_transform(eeg=clip_sample,
                                          baseline=trial_baseline_sample)
                    t_eeg = t['eeg']
                    t_baseline = t['baseline']

                # put baseline signal into IO
                if not 'baseline_id' in trial_meta_info:
                    trial_base_id = f'{file_name}_{write_pointer}'
                    yield {'eeg': t_baseline, 'key': trial_base_id}
                    write_pointer += 1
                    trial_meta_info['baseline_id'] = trial_base_id

                clip_id = f'{file_name}_{write_pointer}'
                write_pointer += 1

                # record meta info for each signal
                record_info = {
                    'start_at': start_at,
                    'end_at': end_at,
                    'clip_id': clip_id
                }
                record_info.update(trial_meta_info)
                yield {'eeg': t_eeg, 'key': clip_id, 'info': record_info}

                start_at = start_at + step
                end_at = start_at + dynamic_chunk_size

    def set_records(self, root_path: str = './data_preprocessed_python',
        **kwargs):
        return os.listdir(root_path)

    def __getitem__(self, index: int) -> Tuple:
        info = self.read_info(index)
        eeg_index = str(info['clip_id'])
        eeg_record = str(info['_record_id'])
        eeg = self.read_eeg(eeg_record, eeg_index)

        baseline_index = str(info['baseline_id'])
        baseline = self.read_eeg(eeg_record, baseline_index)

        signal = eeg
        label = info

        if self.online_transform:
            signal = self.online_transform(eeg=eeg, baseline=baseline)['eeg']

        if self.label_transform:
            label = self.label_transform(y=info)['y']

        return signal, label

    @property
    def repr_body(self) -> Dict:
        return dict(
            super().repr_body, **{
                'root_path': self.root_path,
                'chunk_size': self.chunk_size,
                'overlap': self.overlap,
                'num_channel': self.num_channel,
                'num_baseline': self.num_baseline,
                'baseline_chunk_size': self.baseline_chunk_size,
                'online_transform': self.online_transform,
                'offline_transform': self.offline_transform,
                'label_transform': self.label_transform,
                'before_trial': self.before_trial,
                'after_trial': self.after_trial,
                'num_worker': self.num_worker,
                'verbose': self.verbose,
                'io_size': self.io_size
            })