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A Link between the Increase in Electroencephalographic Coherence and Performance Improvement in Operating a

Irma Nayeli Angulo-Sherman¹, David Gutiérrez¹

¹Centro de Investigación y de Estudios Avanzados (CINVESTAV), Unidad Monterrey, 66600 Apodaca, NL, Mexico.

Computational Intelligence and Neuroscience

|August 21, 2015

Summary

Increased electroencephalographic (EEG) coherence correlates with improved brain-computer interface (BCI) accuracy during motor imagery tasks. This finding aids in evaluating BCI performance and selecting effective feedback methods.

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Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Brain-computer interfaces (BCIs) enable control through neural signals.
Motor imagery is a common BCI control strategy.
Evaluating BCI performance and optimizing training is crucial.

Purpose of the Study:

To investigate the relationship between electroencephalographic (EEG) coherence and BCI accuracy.
To assess the impact of different training paradigms (visual, auditory, FES) on BCI performance.
To identify brain regions associated with improved BCI control.

Main Methods:

Volunteers trained using visual, auditory, or functional electrical stimulation (FES) feedback.
Assessed BCI accuracy post-training.
Calculated event-related coherence (ErCoh) between EEG sensor pairs.
Utilized one-way ANOVA and multiple comparison tests for statistical analysis.

Main Results:

A significant positive correlation was found between increased ErCoh and improved BCI accuracy.
This correlation was most prominent in the centrofrontal and centroparietal brain regions.
Training paradigms influenced BCI performance and coherence patterns.

Conclusions:

Event-related coherence is a reliable indicator of BCI performance during motor imagery.
Centrofrontal and centroparietal regions are key areas for BCI control via motor imagery.
Findings support the development of new BCI evaluation techniques and feedback selection strategies.