Filtered bank n-class SSVEP paradigm
SSVEP paradigm with multiple narrow bandpass filters, centered around the frequencies of considered events. Metric is ‘roc-auc’ if 2 classes and ‘accuracy’ if more.
filters (list of list | None (default None)) – If None, bandpass set around freqs of events with [f_n-0.5, f_n+0.5]
events (List of str,) – List of stimulation frequencies. If None, use all stimulus found in the dataset.
n_classes (int or None (default 2)) – Number of classes each dataset must have. All dataset classes if None
tmin (float (default 0.0)) – Start time (in second) of the epoch, relative to the dataset specific task interval e.g. tmin = 1 would mean the epoch will start 1 second after the begining of the task as defined by the dataset.
tmax (float | None, (default None)) – End time (in second) of the epoch, relative to the begining of the dataset specific task interval. tmax = 5 would mean the epoch will end 5 second after the begining of the task as defined in the dataset. If None, use the dataset value.
baseline (None | tuple of length 2) – The time interval to consider as “baseline” when applying baseline correction. If None, do not apply baseline correction. If a tuple (a, b), the interval is between a and b (in seconds), including the endpoints. Correction is applied by computing the mean of the baseline period and subtracting it from the data (see mne.Epochs)
channels (list of str | None (default None)) – List of channel to select. If None, use all EEG channels available in the dataset.
resample (float | None (default None)) – If not None, resample the eeg data with the sampling rate provided.
Property that define the list of compatible datasets
Property that defines scoring metric (e.g.
get_data(dataset[, subjects, return_epochs])
Return the data for a list of subject.
Verify the dataset is compatible with the paradigm.
Prepare processing of raw files
process_raw(raw, dataset[, return_epochs])
Process one raw data file.