کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4973602 1451647 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 33, March 2017, Pages 109-118
نویسندگان
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