moabb.paradigms.RestingStateToP300Adapter#
- class moabb.paradigms.RestingStateToP300Adapter(fmin=1, fmax=35, tmin=10, tmax=50, resample=128, **kwargs)[source]#
Adapter to the P300 paradigm for resting state experiments.
It implements a SinglePass processing as for P300, except that: - the name of the event is free (it is not enforced to Target/NonTarget as for P300) - the default values are different. In particular, the length of the epochs is larger.
- Parameters
fmin (float (default 1)) – cutoff frequency (Hz) for the high pass filter
fmax (float (default 35)) – cutoff frequency (Hz) for the low pass filter
events (List of str | None (default None)) – event to use for epoching. If None, default to all events defined in the dataset.
tmin (float (default 10s)) – 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 50s)) – 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.
resample (float | None (default 128)) – If not None, resample the eeg data with the sampling rate provided.
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.
- is_valid(dataset)[source]#
Verify the dataset is compatible with the paradigm.
This method is called to verify dataset is compatible with the paradigm.
This method should raise an error if the dataset is not compatible with the paradigm. This is for example the case if the dataset is an ERP dataset for motor imagery paradigm, or if the dataset does not contain any of the required events.
- Parameters
dataset (dataset instance) – The dataset to verify.
- property scoring#
Property that defines scoring metric (e.g. ROC-AUC or accuracy or f-score), given as a sklearn-compatible string or a compatible sklearn scorer.
