این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Medical Signals and Sensors، جلد ۱۲، شماره ۱، صفحات ۴۰-۴۷
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Electroencephalography-based brain–Computer interface motor imagery classification
چکیده انگلیسی مقاله Background: Advances in the medical applications of brain–computer interface, like the motor imagery systems, are highly contributed to making the disabled live better. One of the challenges with such systems is to achieve high classification accuracy. Methods: A highly accurate classification algorithm with low computational complexity is proposed here to classify different motor imageries and execution tasks. An experimental study is performed on two electroencephalography datasets (Iranian Brain–Computer Interface competition [iBCIC] dataset and the world BCI Competition IV dataset 2a) to validate the effectiveness of the proposed method. For lower complexity, the common spatial pattern is applied to decrease the 64 channel signal to four components, in addition to increase the class separability. From these components, first, some features are extracted in the time and time–frequency domains, and next, the best linear combination of these is selected by adopting the stepwise linear discriminant analysis (LDA) method, which are then applied in training and testing the classifiers including LDA, random forest, support vector machine, and K nearest neighbors. The classification strategy is of majority voting among the results of the binary classifiers. Results: The experimental results indicate that the proposed algorithm accuracy is much higher than that of the winner of the first iBCIC. As to dataset 2a of the world BCI competition IV, the obtained results for subjects 6 and 9 outperform their counterparts. Moreover, this algorithm yields a mean kappa value of 0.53, which is higher than that of the second winner of the competition. Conclusion: The results indicate that this method is able to classify motor imagery and execution tasks in both effective and automatic manners.
کلیدواژه‌های انگلیسی مقاله Brain–computer-interface, electroencephalography, linear discriminant analysis, motor imagery, pattern recognition
نویسندگان مقاله | Ehsan Mohammadi
Department of Biomedical Engineering, Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran


| Parisa Ghaderi Daneshmand
Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA


| Seyyed Mohammad Sadegh Moosavi Khorzoog


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/601
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده Original Articles
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات