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Assessment and Communication for People with Disorders of Consciousness
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Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved

Frigyes Samuel Racz1,2,3, Satyam Kumar4, Zalan Kaposzta2

  • 1Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States.

Frontiers in Neuroscience
|March 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new brain-computer interface (BCI) decoder using Detrended Cross-Correlation Analysis (DCCA) with Riemannian geometry-based classification (RGBC). The DCCA-enhanced RGBC significantly improves BCI performance for motor imagery tasks in both offline and online settings.

Keywords:
Reimannian geometrybrain-computer interfacedetrended cross-correlation analysisdetrended fluctuation analysisfractal connectivitymotor imageryonline

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Riemannian geometry-based classification (RGBC) is effective for brain-computer interfaces (BCIs) using electroencephalography (EEG) due to its ability to handle non-stationarities.
  • Traditional RGBC often uses sample covariance matrices (SCMs), which may not fully capture covariance estimation components like regional trends.
  • Detrended Cross-Correlation Analysis (DCCA) can estimate signal covariance structure but is computationally intensive; however, an online implementation enables real-time use.

Purpose of the Study:

  • To propose and evaluate replacing SCM with DCCA matrices as input for RGBC in BCIs.
  • To assess the impact of this DCCA-enhanced RGBC on both offline and online BCI performance.
  • To investigate the neurophysiological insights provided by the DCCA-based decoding approach.

Main Methods:

  • Proposed a decoding pipeline using DCCA matrices as input for RGBC.
  • Evaluated the pipeline offline on EEG data from 18 individuals performing motor imagery (MI).
  • Conducted online BCI experiments with 8 participants and tested on a public multi-class MI-BCI dataset.

Main Results:

  • The DCCA-based decoder significantly outperformed vanilla RGBC and other MI-detection methods in offline evaluations.
  • Online evaluation demonstrated real-time computation of DCCA matrices for 22-channel EEG, achieving high command delivery (κ=0.7409) and MI detection (κ=0.5200).
  • Post-hoc analysis revealed characteristic connectivity patterns and increased fractal scaling exponents in contralateral motor cortices during MI.

Conclusions:

  • Combining DCCA with RGBC creates a robust and effective decoder for BCIs.
  • The DCCA-enhanced approach improves upon SCM-based methods and offers valuable neurophysiological information.
  • This method shows significant promise for real-time BCI applications and understanding neural processes.