Datasets
Datasets in the context of Brain-Computer Interfaces (BCI) are collections of recorded brain signals, often accompanied by metadata such as event markers, channel information, and subject details. These datasets are essential for developing and testing algorithms that interpret brain activity, enabling applications like neurofeedback, prosthetics control, and communication systems for individuals with disabilities.
EEG Dictionary
All following functions returns a dictionary that represents the eeg signals and some useful information about the dataset itself. The dictionary have the following keys:
X: EEG data as a numpy array. The data have the following shape: (n_trials, n_eletrode, n_times)
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.