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The Riemannian Means Field Classifier for EEG-Based BCI Data.

Anton Andreev1, Gregoire Cattan2, Marco Congedo1

  • 1GIPSA-Lab, Université Grenoble Alpes, CNRS, Grenoble INP, 38000 Grenoble, France.

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Summary
This summary is machine-generated.

This study introduces an improved classifier for electroencephalography-based brain-computer interfaces (BCIs). The enhanced method, using power means, outperforms the standard Riemannian minimum distance to mean (MDM) classifier.

Keywords:
BCIEEGP300Riemannian geometryclassificationmotor imagery

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • The Riemannian minimum distance to mean (MDM) classifier is widely recognized for its robustness and accuracy in electroencephalography-based brain-computer interfaces (BCIs).
  • MDM classifiers are known for their simplicity, deterministic nature, noise robustness, computational efficiency, and suitability for transfer learning.
  • Current MDM training relies on computing a geometric mean of symmetric positive-definite (SPD) matrices for each class.

Purpose of the Study:

  • To propose an enhanced version of the Riemannian MDM classifier.
  • To improve BCI performance by utilizing power means of SPD matrices instead of solely the geometric mean.
  • To validate the proposed classifier's efficacy across diverse BCI paradigms and a large participant cohort.

Main Methods:

  • The study proposes a novel classifier that incorporates multiple power means of SPD matrices.
  • The enhanced classifier was evaluated using 20 public BCI databases (10 motor-imagery, 10 P300 paradigms).
  • A total of 587 individuals' data were analyzed to assess classifier performance.

Main Results:

  • The proposed power mean-based classifier demonstrated superior performance compared to the standard MDM classifier.
  • The enhanced classifier achieved performance levels approaching the state-of-the-art.
  • The classifier maintained the simplicity and deterministic characteristics of the original MDM.

Conclusions:

  • The proposed enhancement to the Riemannian MDM classifier offers improved accuracy for EEG-based BCIs.
  • Utilizing power means of SPD matrices is an effective strategy for enhancing BCI classification.
  • The open-source release of the code will facilitate reproducible research in the BCI field.