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Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces.

Keum-Shik Hong1,2, M Jawad Khan2, Melissa J Hong3

  • 1Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea.

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|July 14, 2018
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Summary
This summary is machine-generated.

This study reviews hybrid brain-computer interface (BCI) frameworks using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients. Proper brain region and feature selection enhance BCI accuracy for LIS patients.

Keywords:
brain-computer interfaceclassificationelectroencephalographyfeature extractionfunctional near-infrared spectroscopylocked-in syndrome patient

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Locked-in syndrome (LIS) presents significant communication challenges.
  • Brain-computer interfaces (BCIs) offer a potential communication pathway for LIS patients.
  • Hybrid BCI systems combining functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) show promise for improved performance.

Purpose of the Study:

  • To investigate and review existing brain-computer interface (BCI) frameworks for locked-in syndrome (LIS) patients.
  • To identify suitable brain regions, tasks, and signal processing methods for hybrid fNIRS-EEG BCIs.
  • To propose future research directions for enhancing BCI efficacy in LIS.

Main Methods:

  • Comprehensive literature review of brain tasks, channel selection, feature extraction, and classification algorithms for hybrid fNIRS-EEG BCIs.
  • Categorization of patient impairments to assess BCI suitability.
  • Analysis of brain activity and signal characteristics for fNIRS and EEG.

Main Results:

  • The prefrontal cortex is identified as a suitable brain region.
  • Mental arithmetic and word formation tasks are suitable for LIS patients.
  • A combination of fNIRS signal peaks/means and EEG band powers, with linear discriminant analysis, shows promise for classification.

Conclusions:

  • Optimizing brain region identification and feature selection are crucial for improving BCI classification accuracy.
  • Further research into vector phase analysis for multi-command classification is recommended.
  • A novel hybrid fNIRS-EEG BCI scheme is proposed, incorporating brain therapy for LIS patients.