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A Computationally Efficient Method for Hybrid EEG-fNIRS BCI Based on the Pearson Correlation.

Mustafa A H Hasan1, Muhammad U Khan1, Deepti Mishra2

  • 1Department of Mechatronics Engineering, Atilim University, Ankara, Turkey.

Biomed Research International
|September 14, 2020
PubMed
Summary

This study introduces a new method for selecting channels in hybrid brain-computer interface (BCI) systems, combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The approach reduces computational load while maintaining high accuracy for motor imagery tasks.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Hybrid brain-computer interfaces (BCI) integrate electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for improved performance.
  • Channel selection is critical for optimizing hybrid BCI system efficiency and accuracy.
  • Existing methods face challenges with system complexity and computational time.

Purpose of the Study:

  • To propose a novel channel selection method for hybrid EEG-fNIRS BCIs.
  • To reduce computational burden and complexity in hybrid BCI systems.
  • To enhance the reliability and accuracy of motor imagery classification.

Main Methods:

  • Simultaneous recording of EEG and fNIRS signals.
  • Novel channel selection using Pearson product-moment correlation coefficient to identify highly correlated channels per hemisphere.
  • Extraction of four statistical features and their combinations for classification.
  • Utilized K-Nearest Neighbors (KNN) and Tree classifiers.

Main Results:

  • The proposed channel selection method significantly reduces computational burden.
  • High reliability and classification accuracy comparable to existing literature were achieved.
  • Demonstrated the effectiveness of Pearson correlation for hybrid EEG-fNIRS channel selection.

Conclusions:

  • The novel channel selection approach effectively optimizes hybrid EEG-fNIRS BCIs.
  • This method offers a computationally efficient solution without compromising classification accuracy.
  • Paves the way for more practical and accessible hybrid BCI applications.