Detection of Highly Motivated Time Segments in Brain Computer Interface Signals


AYDEMİR Ö.

IETE JOURNAL OF RESEARCH, vol.66, no.1, pp.3-13, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 66 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.1080/03772063.2018.1476190
  • Title of Journal : IETE JOURNAL OF RESEARCH
  • Page Numbers: pp.3-13
  • Keywords: 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

Abstract

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.