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Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear

Jaeyoung Shin1, Chang-Hwan Im2

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|March 21, 2020
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Summary

Ensemble classifiers significantly improved accuracy and bitrate for near-infrared spectroscopy brain-computer interfaces (NIRS-BCIs). This study demonstrates their effectiveness, addressing a gap in NIRS-BCI research.

Keywords:
bootstrap aggregatingbrain-computer interfaceensemble learningnear-infrared spectroscopypattern classification

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Ensemble classifiers enhance machine learning performance.
  • Electroencephalography-brain-computer interfaces (EEG-BCIs) commonly use ensemble methods.
  • Near-infrared spectroscopy-brain-computer interfaces (NIRS-BCIs) have rarely explored ensemble classifiers.

Purpose of the Study:

  • To systematically evaluate the efficacy of ensemble classifiers for NIRS-BCIs.
  • To investigate the impact of bootstrap aggregating with linear discriminant analysis on NIRS-BCI performance.
  • To determine if ensemble methods improve classification accuracy and data transfer rates in NIRS-BCIs.

Main Methods:

  • Utilized four NIRS-BCI datasets.
  • Employed linear discriminant analysis ensemble classifiers.
  • Applied bootstrap aggregating (bagging) technique.

Main Results:

  • Achieved significant (or marginally significant) increases in classification accuracy across all four datasets.
  • Observed significant increases in bitrate for two of the four datasets.
  • Demonstrated the effectiveness of ensemble classifiers in enhancing NIRS-BCI performance.

Conclusions:

  • Ensemble classifiers, specifically bootstrap aggregating with LDA, are effective for NIRS-BCIs.
  • These methods offer a promising approach to improve NIRS-BCI accuracy and data throughput.
  • Further research into ensemble methods for NIRS-BCIs is warranted.