moabb.paradigms.LeftRightImagery

class moabb.paradigms.LeftRightImagery(fmin=8, fmax=32, events=None, tmin=0.0, tmax=None, baseline=None, channels=None, resample=None, scorer=None, overlap=None)

Motor Imagery for left hand/right hand classification.

Metric is ‘roc_auc’ by default

Parameters:

• fmin (float (default 8)) – cutoff frequency (Hz) for the high pass filter.

• fmax (float (default 32)) – cutoff frequency (Hz) for the low pass filter.

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.

Examples using moabb.paradigms.LeftRightImagery

Tutorial 0: Getting Started

Tutorial 0: Getting Started

Tutorial 1: Simple Motor Imagery

Tutorial 1: Simple Motor Imagery

Tutorial 2: Using multiple datasets

Tutorial 2: Using multiple datasets

Tutorial 3: Benchmarking multiple pipelines

Tutorial 3: Benchmarking multiple pipelines

Tutorial 4: Creating a dataset class

Tutorial 4: Creating a dataset class

Cross-Session Motor Imagery

Cross-Session Motor Imagery

Cross-Session on Multiple Datasets

Cross-Session on Multiple Datasets

Cache on disk intermediate data processing states

Cache on disk intermediate data processing states

Explore Paradigm Object

Explore Paradigm Object

Benchmarking with MOABB showing the CO2 footprint

Benchmarking with MOABB showing the CO2 footprint

Benchmarking with MOABB

Benchmarking with MOABB

Tutorial: Within-Session Splitting on Real MI Dataset

Tutorial: Within-Session Splitting on Real MI Dataset

Pipelines using the mne-features library

Pipelines using the mne-features library

FilterBank CSP versus CSP

FilterBank CSP versus CSP

Playing with the pre-processing steps

Playing with the pre-processing steps

Select Electrodes and Resampling

Select Electrodes and Resampling

Statistical Analysis

Statistical Analysis

Within Session Motor Imagery with Learning Curve

Within Session Motor Imagery with Learning Curve