moabb.paradigms.motor_imagery.SinglePass#

class moabb.paradigms.motor_imagery.SinglePass(fmin=8, fmax=32, **kwargs)[source]#

Single Bandpass filter motor Imagery.

Motor imagery paradigm with only one bandpass filter (default 8 to 32 Hz)

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

  • 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 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.

Examples using moabb.paradigms.motor_imagery.SinglePass#

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

Fixed interval windows processing

Fixed interval windows processing

Benchmarking with MOABB

Benchmarking with MOABB

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

Pipelines using the mne-features library

Pipelines using the mne-features library

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