Optimization of preprocessing stage in EEG based BCI systems in terms of accuracy and timing cost. (May 2021)

• Record Type: Journal Article
• Authors: Dagdevir, Eda; Tokmakci, Mahmut
• Is Part Of: Biomedical signal processing and control. Volume 67(2021)
• Publication Date: 2021-05
• Subjects: Optimization -- Biomedical signal processing -- BCI -- EEG -- Taguchi method -- ANOVA -- Grey relation analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102548
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: It is aimed to eliminate the confusion by optimizing the BCI preprocessing stages. The time window-step size, time interval, theta, mu, beta bands stages are optimized in terms of accuracy and timing cost. Preprocessing stages are performed in the experimental plan created by Taguchi method using the BCI Competition IV-2b dataset. The features are extracted from the preprocessed data with the Hjorth algorithm and are classified with the SVM classifier. Optimum combination of stages is determined with grey relation analysis in terms of accuracy and timing cost, simultaneously. Abstract: Performance of the motor imagery-based brain computer interface (MI-BCI) systems has been tried to improve by the researchers with novel approaches and methods used on preprocessing stages. In this study, the preprocessing stages are optimized to improve the performance of MI-BCI systems in terms of the accuracy and the timing cost. Taguchi method is adopted for the optimization study. Time window-step size, time interval, theta frequency band, and mu and beta frequency bands are considered as controllable factors in the preprocessing stage. The preprocessing stages are performed according to an experimental plan created by the Taguchi method. The study is applied on BCI Competition IV-2b dataset. The features are extracted from the data with the Hjorth algorithm and classified by using the SVM classifier. The statistical significance and relatively contribution effects of factors on the performance parameters are tested with ANOVA. As a result, optimum combinations of the preprocessing stage factors providing the highest accuracy and the lowest timing cost are both individually and simultaneously revealed. In addition, it is concluded that the most effective factor on the preprocessing stage is determined as time window-step size. Consequently, the results of various combinations of the preprocessing stages on the MI-BCI performance are revealed in terms of both the accuracy and the timing cost.