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

AKMAN AYDIN E.

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

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 195
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.cmpb.2020.105535
  • Dergi Adı: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Derginin Tarandığı İndeksler: 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
  • Anahtar Kelimeler: Near-infrared spectroscopy, Mental arithmetic, Motor imagery, Brain-computer interfaces, Feature selection, Stepwise regression analysis, ReliefF algorithm, SIGNALS, BCI, COMMUNICATION
  • Gazi Üniversitesi Adresli: Evet

Özet

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.