tailieunhanh - Báo cáo hóa học: " Research Article Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition, Clustering, and Support Vector Machine"

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: Research Article Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition, Clustering, and Support Vector Machine | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 592742 8 pages doi 2008 592742 Research Article Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition Clustering and Support Vector Machine Zhisong Wang 1 Alexander Maier 2 Nikos K. Logothetis 3 and Hualou Liang1 1 School of Health Information Sciences University of Texas Health Science Center at Houston 7000 Fannin Suite 600 Houston TX 77030 USA 2 Unit on Cognitive Neurophysiology and Imaging National Institute of Health Building 49 Room B2J-45 MSC-4400 49 Convent Dr. Bethesda MD 20892 USA 3 Max Planck Institut fur biologische Kybernetik Spemannstra e 38 72076 Tubingen Germany Correspondence should be addressed to Hualou Liang Received 23 August 2007 Revised 25 January 2008 Accepted 10 March 2008 Recommended by Nii Attoh-Okine We propose an empirical mode decomposition EMD- based method to extract features from the multichannel recordings of local field potential LFP collected from the middle temporal MT visual cortex in a macaque monkey for decoding its bistable structure-from-motion SFM perception. The feature extraction approach consists of three stages. First we employ EMD to decompose nonstationary single-trial time series into narrowband components called intrinsic mode functions IMFs with time scales dependent on the data. Second we adopt unsupervised K-means clustering to group the IMFs and residues into several clusters across all trials and channels. Third we use the supervised common spatial patterns CSP approach to design spatial filters for the clustered spatiotemporal signals. We exploit the support vector machine SVM classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that the CSP feature of the cluster in the gamma frequency band outperforms the features in other frequency bands and leads to the best decoding .