Source code for torcheeg.datasets.module.emotion_recognition.amigos
import logging
import os
import re
from typing import Any, Callable, Dict, List, Tuple, Union
import numpy as np
import scipy.io as scio
from ....utils import get_random_dir_path
from ..base_dataset import BaseDataset
log = logging.getLogger('torcheeg')
[docs]class AMIGOSDataset(BaseDataset):
r'''
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 :obj:`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:
.. code-block:: python
from torcheeg.datasets import AMIGOSDataset
from torcheeg import transforms
from torcheeg.datasets.constants.emotion_recognition.amigos import AMIGOS_CHANNEL_LOCATION_DICT
dataset = AMIGOSDataset(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[4, 9, 9]),
# coresponding baseline signal (torch.Tensor[4, 9, 9]),
# label (int)
Another example dataset for CNN-based methods:
.. code-block:: python
from torcheeg.datasets import AMIGOSDataset
from torcheeg import transforms
dataset = AMIGOSDataset(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:
.. code-block:: python
from torcheeg.datasets import AMIGOSDataset
from torcheeg import transforms
from torcheeg.transforms.pyg import ToG
from torcheeg.datasets.constants.emotion_recognition.amigos import AMIGOS_ADJACENCY_MATRIX
dataset = AMIGOSDataset(root_path='./data_preprocessed',
online_transform=transforms.Compose(
[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)
Args:
root_path (str): Downloaded data files in matlab (unzipped data_preprocessed.zip) formats (default: :obj:`'./data_preprocessed'`)
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 14 channels are EEG signals. (default: :obj:`14`)
num_trial (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: :obj:`16`)
skipped_subjects (int): The participant ID to be removed because there are some invalid data in the preprocessed version. (default: :obj:`[9, 12, 21, 22, 23, 24, 33]`)
num_baseline (int): Number of baseline signal chunks used. (default: :obj:`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: :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. If set to None, a random path will be generated. (default: :obj:`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: :obj:`1048576`)
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. When io_mode is set to :obj:`memory`, memory 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`)
'''
def __init__(self,
root_path: str = './data_preprocessed',
chunk_size: int = 128,
overlap: int = 0,
num_channel: int = 14,
num_trial: int = 16,
skipped_subjects: List[int] = [9, 12, 21, 22, 23, 24, 33],
num_baseline: 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,
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: Union[None, str] = None,
io_size: int = 1048576,
io_mode: str = 'lmdb',
num_worker: int = 0,
verbose: bool = True):
# pass all arguments to super class
if io_path is None:
io_path = get_random_dir_path(dir_prefix='datasets')
params = {
'root_path': root_path,
'chunk_size': chunk_size,
'overlap': overlap,
'num_channel': num_channel,
'num_trial': num_trial,
'skipped_subjects': skipped_subjects,
'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
}
super().__init__(**params)
# save all arguments to __dict__
self.__dict__.update(params)
@staticmethod
def read_record(record: str,
root_path: str = './data_preprocessed', **kwargs) -> Dict:
data = scio.loadmat(os.path.join(root_path, record),
verify_compressed_data_integrity=False)
samples = data['joined_data'][
0] # trial (20), timestep(n*128), channel(17) (14 channels are EEGs)
# label file
labels = data['labels_selfassessment'][
0] # trial (20), label of different dimensions ((1, 12))
return {
'samples': samples,
'labels': labels
}
@staticmethod
def fake_record(record: str, **kwargs) -> Dict:
num_trials = 20
trial_length = 10 * 128
num_channels = 17
num_labels = 12
samples = []
labels = []
for _ in range(num_trials):
trial_sample = np.random.rand(trial_length, num_channels)
trial_label = np.random.uniform(1, 9, (1, num_labels))
samples.append(trial_sample)
labels.append(trial_label)
return {
'record': 'Data_Preprocessed_P1.mat',
'samples': np.array(samples),
'labels': np.array(labels)
}
@staticmethod
def process_record(
record: str,
samples: np.ndarray,
labels: np.ndarray,
chunk_size: int = 128,
overlap: int = 0,
num_channel: int = 14,
num_trial: int = 16,
skipped_subjects: List[int] = [9, 12, 21, 22, 23, 24, 33],
num_baseline: int = 5,
baseline_chunk_size: int = 128,
before_trial: Union[None, Callable] = None,
offline_transform: Union[None, Callable] = None,
**kwargs):
subject = int(
re.findall(r'Data_Preprocessed_P(\d*).mat',
record)[0]) # subject (40)
if subject in skipped_subjects:
return
write_pointer = 0
max_len = len(samples)
if not (num_trial <= 0):
max_len = min(len(samples), num_trial)
# loop for each trial
for trial_id in range(max_len):
# extract baseline signals
trial_samples = samples[trial_id]
# record the common meta info
trial_meta_info = {'subject_id': subject, 'trial_id': trial_id}
trial_rating = labels[trial_id][
0] # label of different dimensions (12)
# missing values
if (not sum(trial_samples.shape)) or (not sum(trial_rating.shape)):
# 3 of the participants (08,24,28<->32) of the previous experiment did not watch a set of 4 long affective
if sum(trial_samples.shape) != sum(trial_rating.shape):
log.info(
f'Find EEG signals without labels, or labels without EEG signals. Please check the {trial_id + 1}-th experiment of the {subject}-th subject in the file {record}. TorchEEG currently skipped the mismatched data.'
)
continue
trial_samples = trial_samples.swapaxes(
1, 0) # channel(17), timestep(n*128)
if before_trial:
trial_samples = before_trial(trial_samples)
for label_index, label_name in enumerate([
'arousal', 'valence', 'dominance', 'liking', 'familiarity',
'neutral', 'disgust', 'happiness', 'surprise', 'anger',
'fear', 'sadness'
]):
trial_meta_info[label_name] = trial_rating[label_index]
# extract baseline signals
trial_baseline_sample = trial_samples[:num_channel, :
baseline_chunk_size *
num_baseline] # channel(14), timestep(5*128)
trial_baseline_sample = trial_baseline_sample.reshape(
num_channel, num_baseline,
baseline_chunk_size).mean(axis=1) # channel(14), timestep(128)
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[:num_channel, 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'{record}_{write_pointer}'
yield {'eeg': t_baseline, 'key': trial_base_id}
write_pointer += 1
trial_meta_info['baseline_id'] = trial_base_id
clip_id = f'{record}_{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', **kwargs):
assert os.path.exists(
root_path
), f'root_path ({root_path}) does not exist. Please download the dataset and set the root_path to the downloaded path.'
return os.listdir(root_path)
def __getitem__(self, index: int) -> Tuple[any, any, int, int, int]:
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_trial': self.num_trial,
'skipped_subjects': self.skipped_subjects,
'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
})
