Comparative Analysis of NIRS-EEG Motor Imagery Data Using Features from Spatial, Spectral and Temporal Domain

Hyun Ji Kim, In Nea Wang, Young Tak Kim, Hakseung Kim, Dong Joo Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Brain-computer interface (BCI) systems, which provide users with an additional channel to communicate with external devices, have been mainly developed using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). To complement each modality's pros and cons, various hybrid NIRS-EEG studies have been investigated. However, most studies focused on enhancing the classification accuracy rather than analyzing the characteristics of used features. This study aimed to investigate whether EEG features from spatial, temporal, and spectral domains would exhibit the diverse efficacy in hybrid NIRS-EEG BCI. Open access NIRS and EEG recordings of left/right hand gripping imagery from twenty-nine healthy subjects were utilized. Common spatial patterns (CSP), time domain parameters (TDP), and power spectral density (PSD) were separately employed with NIRS to evaluate the discrimination performance. Within dataset, NIRS with CSP showed the highest classification accuracy with linear support vector machine (LSVM) classifier (mean accuracy, 71.4%). For kernel SVM (KSVM) classifiers, mean accuracy of NIRS with TDP features was lower than accuracy of only NIRS features (mean accuracy, NIRS: 53.1% and NIRS with TDP: 50.5%). The findings suggested that binary motor imagery tasks, which involve localized brain activation, could be enhanced by applying features including rich spatial information.

Original languageEnglish
Title of host publication8th International Winter Conference on Brain-Computer Interface, BCI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147079
DOIs
Publication statusPublished - 2020 Feb 1
Event8th International Winter Conference on Brain-Computer Interface, BCI 2020 - Gangwon, Korea, Republic of
Duration: 2020 Feb 262020 Feb 28

Publication series

Name8th International Winter Conference on Brain-Computer Interface, BCI 2020

Conference

Conference8th International Winter Conference on Brain-Computer Interface, BCI 2020
CountryKorea, Republic of
CityGangwon
Period20/2/2620/2/28

Keywords

  • Brain-computer interface (BCI)
  • electroencephalography (EEG)
  • hybrid BCI
  • motor imagery
  • near-infrared spectroscopy (NIRS)

ASJC Scopus subject areas

  • Behavioral Neuroscience
  • Cognitive Neuroscience
  • Artificial Intelligence
  • Human-Computer Interaction

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    Kim, H. J., Wang, I. N., Kim, Y. T., Kim, H., & Kim, D. J. (2020). Comparative Analysis of NIRS-EEG Motor Imagery Data Using Features from Spatial, Spectral and Temporal Domain. In 8th International Winter Conference on Brain-Computer Interface, BCI 2020 [9061636] (8th International Winter Conference on Brain-Computer Interface, BCI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI48061.2020.9061636