moabb.paradigms.base.BaseParadigm#

class moabb.paradigms.base.BaseParadigm(filters, events: List[str] | None = None, tmin=0.0, tmax=None, baseline=None, channels=None, resample=None, overlap=None, scorer=None)[source]#

Base class for paradigms.

Parameters:

  • events (List of str | None (default None)) – events to use for epoching. If None, default to all events defined in the dataset.

  • scorer (sklearn-compatible string or a compatible sklearn scorer | None (default None)) – If None, and n_classes==2 use the roc_auc, else use accuracy.

abstract 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.base.BaseParadigm#

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 custom datasets

Tutorial 4: Creating custom datasets

Tutorial 5: Combining Multiple Datasets into a Single Dataset

Tutorial 5: Combining Multiple Datasets into a Single Dataset

Cross-Session Motor Imagery

Cross-Session Motor Imagery

Cross-Session on Multiple Datasets

Cross-Session on Multiple Datasets

Cross-Subject SSVEP

Cross-Subject SSVEP

Changing epoch size in P300 VR dataset

Changing epoch size in P300 VR dataset

Within Session P300

Within Session P300

Within Session SSVEP

Within Session SSVEP

Cache on disk intermediate data processing states

Cache on disk intermediate data processing states

Explore Paradigm Object

Explore Paradigm Object

Fixed interval windows processing

Fixed interval windows processing

Benchmarking with MOABB

Benchmarking with MOABB

Benchmarking with MOABB with Grid Search

Benchmarking with MOABB with Grid Search

Tutorial: Within-Session Splitting on Real MI Dataset

Tutorial: Within-Session Splitting on Real MI Dataset

Examples of analysis of a Dreyer2023 A dataset.

Examples of analysis of a Dreyer2023 A dataset.

FilterBank CSP versus CSP

FilterBank CSP versus CSP

GridSearch within a session

GridSearch within a session

Hinss2021 classification example

Hinss2021 classification example

MNE Epochs-based pipelines

MNE Epochs-based pipelines

Spectral analysis of the trials

Spectral analysis of the trials

Playing with the pre-processing steps

Playing with the pre-processing steps

Pseudo-Online Motor Imagery with Sliding Window

Pseudo-Online Motor Imagery with Sliding Window

Riemannian Artifact Rejection as a Pre-processing Step

Riemannian Artifact Rejection as a Pre-processing Step

Select Electrodes and Resampling

Select Electrodes and Resampling

Time-Resolved Decoding with SlidingEstimator

Time-Resolved Decoding with SlidingEstimator

Statistical Analysis and Chance Level Assessment

Statistical Analysis and Chance Level Assessment

Using X y data (epoched data) instead of continuous signal

Using X y data (epoched data) instead of continuous signal

Within Session Motor Imagery with Learning Curve

Within Session Motor Imagery with Learning Curve

Within Session P300 with Learning Curve

Within Session P300 with Learning Curve