Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm. (March 2017)

• Record Type: Journal Article
• Authors: Ghaemi, Alireza; Rashedi, Esmat; Pourrahimi, Ali Mohammad; Kamandar, Mehdi; Rahdari, Farhad
• Is Part Of: Biomedical signal processing and control. Volume 33(2017)
• Page Start: 109
• Page End: 118
• Publication Date: 2017-03
• Subjects: Brain computer interface -- Signal processing -- EEG signals -- Classification -- Channel selection -- Feature extraction -- Improved binary gravitation search algorithm; 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.2016.11.018
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper presents an automatic method for finding optimal channels in Brain Computer Interfaces (BCIs). Improved Binary Gravitation Search Algorithm (IBGSA) is used to automatically detect the effective electroencephalography (EEG) channels in left or right hand classification. Feature extraction process is carried out by analyzing EEG signals in time and wavelet domains. The maximum accuracy of 80% and average accuracy of 76.24% were obtained using Support Vector Machine (SVM) classifier for eight subjects in BCI competition IV dataset. Abstract: This paper presents an automatic method for finding optimal channels in Brain Computer Interfaces (BCIs). Detecting the effective channels in BCI systems is an important problem in reducing the complexity of these systems. In this research, Improved Binary Gravitation Search Algorithm (IBGSA) is used to automatically detect the effective electroencephalography (EEG) channels in left or right hand classification. To do this, at first, data is filtered with a bandpass filter in order to reduce the amount of different types of merged noise. Then, the electrooculography (EOG) and electromyography (EMG) artifacts are corrected based on Blind Source Separation (BSS) algorithm. Data is epoched according to the left or right hand motor imageries and central beta frequency band is isolated for Event Related Synchronization (ERS) analysis. Feature extraction process is carried out by analyzing EEG signals in time and wavelet domains. The logarithmic power of each channel is computed in time domain and the features of mean, mode, median, variance, and standard deviation are calculated in wavelet domain. IBGSA is employed to detect the optimal channels to achieve better classification results. Support Vector Machine (SVM) is used as the classifier. The maximum accuracy of 80% and average accuracy of 76.24% were obtained for eight subjects in BCI competition IV dataset. The results of this research confirm that automatically detecting effective channels can enhance the practical implementation of BCI based systems and reduce the complexity.