Subject-Specific feature selection for near infrared spectroscopy based brain-computer interfaces

AKMAN AYDIN E.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.195, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 195
  • Publication Date: 2020
  • Doi Number: 10.1016/j.cmpb.2020.105535
  • Journal Name: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Keywords: Near-infrared spectroscopy, Mental arithmetic, Motor imagery, Brain-computer interfaces, Feature selection, Stepwise regression analysis, ReliefF algorithm, SIGNALS, BCI, COMMUNICATION
  • Gazi University Affiliated: Yes

Abstract

Background and Objective: Brain-computer interfaces (BCIs) enable people to control an external device by analyzing the brain's neural activity. Functional near-infrared spectroscopy (fNIRS), which is an emerging optical imaging technique, is frequently used in non-invasive BCIs. Determining the subject-specific features is an important concern in enhancing the classification accuracy as well as reducing the complexity of fNIRS based BCI systems. In this study, the effectiveness of subject-specific feature selection on classification accuracy of fNIRS signals is examined.