Determination of Effective Signal Processing Stages for Brain Computer Interface on BCI Competition IV Data Set 2b: A Review Study

Dağdevir E., Tokmakçı M.

IETE JOURNAL OF RESEARCH, vol.69, no.6, pp.3144-3155, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 69 Issue: 6
  • Publication Date: 2023
  • Doi Number: 10.1080/03772063.2021.1914204
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Page Numbers: pp.3144-3155
  • Keywords: Brain computer interface, Classification performance, Electroencephalography, Human-computer interaction, Motor imagery, Signal processing, MOTOR-IMAGERY, FEATURE-EXTRACTION, EEG SIGNAL
  • Kayseri University Affiliated: Yes


Considering the entire BCI system, a big challenge is that information can be extracted from brain signals in a meaningful way. Therefore, most BCI studies are focused on brain signal processing, in which the stages are preprocessing, feature extraction, feature selection, and classification. Since each of the signal processing methods is subject-specific, it is necessary to select a specific subject group, that is, a data set, for an effective signal processing review. In this study, all stages of BCI signal processing studies that used the 2b data set recorded with the EEG method for the BCI Competition IV were compiled and compared comprehensively. To be an effective review, this paper organized into common components and showed how varying the four stages alter classification performance. Classification of performance obtained with the methods in the compiled studies was compared in terms of kappa values. The results demonstrate that combinations of different methods affect and improve the performance. This study presents comprehensive guidance by considering all stages for BCI Competition IV data set 2b. The purpose of the present study was to shed light on research with the aim to enhance BCI performance with signal processing using BCI Competition IV data set 2b.