Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface | EURASIP Journal on Applied Signal Processing 2005 19 3128-3140 2005 David A. Peterson et al. Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface David A. Peterson Department of Computer Science Center for Biomedical Research in Music Molecular Cellular and Integrative Neurosciences Program and Department of Psychology Colorado State University Fort Collins CO 80523 USA Email petersod@ James N. Knight Department of Computer Science Colorado State University Fort Collins CO 80523 USA Email nate@ Michael J. Kirby Department of Mathematics Colorado State University Fort Collins CO 80523 USA Email kirby@ Charles W. Anderson Department of Computer Science and Molecular Cellular and Integrative Neurosciences Program Colorado State University Fort Collins CO 80523 USA Email anderson@ Michael H. Thaut Center for Biomedical Research in Music and Molecular Cellular and Integrative Neurosciences Program Colorado State University Fort Collins CO 80523 USA Email Received 1 February 2004 Revised 14 March 2005 Most EEG-based BCI systems make use of well-studied patterns of brain activity. However those systems involve tasks that indirectly map to simple binary commands such as yes or no or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct yes no BCI from a single session. Blind source separation BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm GA wrapped around a support vector machine SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also BSS transformations of the EEG outperformed the original time series particularly in conjunction with a subset search of both spaces.
