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: A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures | EURASIP Journal on Applied Signal Processing 2005 19 3141-3151 2005 Hindawi Publishing Corporation A Time-Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures Damien Coyle Intelligent Systems Engineering Laboratory School of Computing and Intelligent Systems Faculty of Engineering University of Ulster Magee Campus Derry BT48 7JL UK Email Girijesh Prasad Intelligent Systems Engineering Laboratory School of Computing and Intelligent Systems Faculty of Engineering University of Ulster Magee Campus Derry BT48 7JL UK Email T. M. McGinnity Intelligent Systems Engineering Laboratory School of Computing and Intelligent Systems Faculty of Engineering University of Ulster Magee Campus Derry BT48 7JL UK Email Received 2 February 2004 Revised 4 October 2004 The paper presents an investigation into a time-frequency TF method for extracting features from the electroencephalogram EEG recorded from subjects performing imagination of left- and right-hand movements. The feature extraction procedure FEP extracts frequency domain information to form features whilst time-frequency resolution is attained by localising the fast Fourier transformations FFTs of the signals to specific windows localised in time. All features are extracted at the rate of the signal sampling interval from a main feature extraction FE window through which all data passes. Subject-specific frequency bands are selected for optimal feature extraction and intraclass variations are reduced by smoothing the spectra for each signal by an interpolation IP process. The TF features are classified using linear discriminant analysis LDA . The FE window has potential advantages for the FEP to be applied in an online brain-computer interface BCI . The approach achieves good performance when quantified by classification accuracy CA rate information transfer IT rate and mutual .
