moabb.paradigms.ssvep.BaseSSVEP#

class moabb.paradigms.ssvep.BaseSSVEP(filters=((7, 45),), events=None, n_classes=None, tmin=0.0, tmax=None, baseline=None, channels=None, resample=None)[source]#

Base SSVEP Paradigm.

Parameters:

  • filters (list of list | None (default [7, 45])) – Bank of bandpass filter to apply.

  • events (list of str | None (default None)) – List of stimulation frequencies. If None, use all stimulus found in the dataset.

  • n_classes (int or None (default None)) – 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 beginning of the task as defined by the dataset.

  • tmax (float | None, (default None)) – End time (in second) of the epoch, relative to the beginning of the dataset specific task interval. tmax = 5 would mean the epoch will end 5 second after the beginning 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 datasets#

List of datasets valid for the paradigm.

is_valid(dataset)[source]#

Check if dataset is valid for the SSVEP paradigm.

prepare_process(dataset)[source]#

Prepare dataset for processing, and using events if needed.

This function is called before the processing function, and is used to prepare the dataset for processing. This includes: get the events used for the paradigm, and set the filters if needed. :param dataset: Dataset to prepare. :type dataset: moabb.datasets.base.BaseDataset

property scoring#

Return the default scoring method for this paradigm.

If n_classes use the roc_auc, else use accuracy. More details about this default scoring method can be found in the original moabb paper.

used_events(dataset)[source]#

Return the mne events used for the dataset.