"""
Description
-----------
This module provides functionality to load EEG data from the CBCIC dataset.
It processes the data to fit the requirements of the `eegdata` dictionary,
which is used to store and process EEG data.
"""
import numpy as np
import scipy
[docs]
def cbcic(subject: int = 1,
session_list: list = None,
labels: list = ['left-hand', 'right-hand'],
path: str = 'data/cbcic/'):
"""
This function loads EEG data for a specific subject and session from the CBCIC dataset.
It processes the data to fit the structure of the `eegdata` dictionary, which is used
for further processing and analysis.
Parameters
----------
subject : int, optional
Index of the subject to retrieve the data from. Must be between 1 and 9.
Default is 1.
session_list : list, optional
List of session codes to load. Valid options are 'T' (training) and 'E' (evaluation).
If None, all sessions are loaded. Default is None.
labels : list, optional
List of labels to include in the dataset. Valid options are 'left-hand' and 'right-hand'.
Default is ['left-hand', 'right-hand'].
path : str, optional
Path to the folder containing the dataset files. Default is 'data/cbcic/'.
Returns
-------
dict
A dictionary containing the following keys:
X: EEG data as a numpy array.
y: Labels corresponding to the EEG data.
sfreq: Sampling frequency of the EEG data.
y_dict: Mapping of labels to integers.
events: Dictionary describing event markers.
ch_names: List of channel names.
tmin: Start time of the EEG data.
Raises
------
ValueError
If any of the input parameters are invalid or if the specified file does not exist.
Examples
--------
Load EEG data for subject 1, all sessions, and default labels:
>>> eeg_data = cbcic(subject=1)
>>> print(eeg_data['X'].shape) # Shape of the EEG data
>>> print(eeg_data['y']) # Labels
"""
# Check if the subject input is valid
if type(subject) != int:
raise ValueError("subject has to be an int type value")
if subject > 9 or subject < 1:
raise ValueError("subject has to be between 1 and 9")
# Check if the session_list input is valid
if type(session_list) != list and session_list is not None:
raise ValueError("session_list has to be a list or None")
if session_list is not None:
for i in session_list:
if i not in ['T', 'E']:
raise ValueError("session_list has to be a sublist of ['T', 'E']")
else:
session_list = ['T', 'E']
# Check if the labels input is valid
if type(labels) != list:
raise ValueError("labels has to be a list type value")
for i in labels:
if i not in ['left-hand', 'right-hand']:
raise ValueError("labels has to be a sublist of ['left-hand', 'right-hand']")
# Check if the path input is valid
if type(path) != str:
raise ValueError("path has to be a str type value")
if path[-1] != '/':
path += '/'
# Set basic parameters of the clinical BCI challenge dataset
sfreq = 512.
events = {'get_start': [0, 3],
'beep_sound': [2],
'cue': [3, 8],
'task_exec': [3, 8]}
ch_names = np.array(["F3", "FC3", "C3", "CP3", "P3", "FCz", "CPz", "F4", "FC4", "C4", "CP4", "P4"])
tmin = 0.
rawData, rawLabels = [], []
for sec in session_list:
file_name = 'parsed_P%02d%s.mat' % (subject, sec)
try:
raw = scipy.io.loadmat(path + file_name)
except:
raise ValueError("The file %s does not exist in the path %s" % (file_name, path))
rawData_ = raw['RawEEGData']
rawLabels_ = np.reshape(raw['Labels'], -1)
rawData_ = np.reshape(rawData_, (rawData_.shape[0], 1, rawData_.shape[1], rawData_.shape[2]))
rawData.append(rawData_)
rawLabels.append(rawLabels_)
X, y = np.concatenate(rawData), np.concatenate(rawLabels)
labels_dict = {1: 'left-hand', 2: 'right-hand'}
y = np.array([labels_dict[i] for i in y])
selected_labels = np.isin(y, labels)
X, y = X[selected_labels], y[selected_labels]
y_dict = {labels[i]: i for i in range(len(labels))}
y = np.array([y_dict[i] for i in y])
return {'X': X,
'y': y,
'sfreq': sfreq,
'y_dict': y_dict,
'events': events,
'ch_names': ch_names,
'tmin': tmin}