Enhancing performance of a hybrid EEG-fNIRS system using channel selection and early temporal features

Rihui Li, Thomas Potter, Weitian Huang, Yingchun Zhang

Research output: Contribution to journalArticle

35 Scopus citations

Abstract

Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties. In this study, we aimed to investigate whether the early temporal information extracted from singular EEG and fNIRS channels on each hemisphere can be used to enhance the accuracy and efficiency of a hybrid EEG-fNIRS BCI system. Eleven healthy volunteers were recruited and underwent simultaneous EEG-fNIRS recording during a motor execution task that included left and right hand movements. Singular EEG and fNIRS channels corresponding to the motor cortices of each hemisphere were selected using a general linear model. Early temporal information was extracted from the EEG channel (0-1 s) along with initial hemodynamic dip information from fNIRS (0-2 s) for classification using a support vector machine (SVM). Results demonstrated a lofty classification accuracy using aminimal number of channels and features derived fromearly temporal information. In conclusion, a hybrid EEG-fNIRS BCI system can achieve higher classification accuracy (91.02 ± 4.08%) and efficiency by integrating their complimentary properties, compared to using EEG (85.64 ± 7.4%) or fNIRS alone (85.55 ± 10.72%). Such a hybrid system can also achieveminimal response lag in application by focusing on rapidly-evolving brain dynamics.

Original languageEnglish (US)
Article number462
JournalFrontiers in Human Neuroscience
Volume11
DOIs
StatePublished - Sep 15 2017

Keywords

  • EEG
  • General linear model
  • Hybrid BCI
  • NIRS
  • Principal component analysis

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Neurology
  • Psychiatry and Mental health
  • Biological Psychiatry
  • Behavioral Neuroscience

Fingerprint Dive into the research topics of 'Enhancing performance of a hybrid EEG-fNIRS system using channel selection and early temporal features'. Together they form a unique fingerprint.

Cite this