moabb.paradigms.RestingStateToP300Adapter#
- class moabb.paradigms.RestingStateToP300Adapter(fmin=1, fmax=35, events=None, tmin=10, tmax=50, baseline=None, channels=None, resample=128, ignore_relabelling=False, scorer=None)[source]#
Adapter to the P300 paradigm for resting state experiments.
It implements a single bandpass 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
tmin (float (default 10)) – 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 50)) – 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.
- 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.
