Detection of Highly Motivated Time Segments in Brain Computer Interface Signals

AYDEMİR Ö.

IETE JOURNAL OF RESEARCH, cilt.66, sa.1, ss.3-13, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 66 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1080/03772063.2018.1476190
  • Dergi Adı: IETE JOURNAL OF RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Sayfa Sayıları: ss.3-13
  • Anahtar Kelimeler: Brain computer interface, Classification accuracy, Distribution of motivation, EEG, CONTINUOUS WAVELET TRANSFORM, BCI COMPETITION 2003, FEATURE-EXTRACTION, FEATURE-SELECTION, MOTOR IMAGERY, EEG SIGNALS, CLASSIFICATION, ECOG, TASKS
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Motivation of a subject, who is associated with the data acquisition of brain computer interface (BCI) experiment, is a very crucial parameter for executing a successful BCI application. This paper proposes a novel method to present the distribution of motivation of a subject during a BCI experiment. The proposed method was successfully applied to the BCI Competition 2003 Data Set III and the BCI Competition 2005 Data Set I using fast Fourier transform-based band power features with a linear discriminant analysis classifier. The results show that not only the motivation of the subject dramatically changes during the trial but also using highly motivated time segments provides 7.86% and 2.00% improvement in the classification accuracy of the BCI Competition 2003 Data Set III and the BCI Competition 2005 Data Set I, respectively.