Journal article
Single-trial classification of bistable perception by integrating empirical mode decomposition, clustering, and support vector machine
EURASIP journal on advances in signal processing, v 2008(1), pp 592742-592742
01 Jan 2008
PMID: 18784852
Abstract
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 performance. We also show that the EMD- based feature extraction can be useful for evoked potential estimation. Our proposed feature extraction approach may have potential for many applications involving nonstationary multivariable time series such as brain-computer interfaces (BCI). Copyright (c) 2008 Zhisong Wang et al.
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Details
- Title
- Single-trial classification of bistable perception by integrating empirical mode decomposition, clustering, and support vector machine
- Creators
- Zhisong Wang - The University of Texas Health Science Center at HoustonAlexander Maler - CognIT (Norway)Nikos K. Logothetis - Max Planck Institute for Biological CyberneticsHualou Liang - The University of Texas Health Science Center at Houston
- Publication Details
- EURASIP journal on advances in signal processing, v 2008(1), pp 592742-592742
- Publisher
- Springer Nature
- Number of pages
- 8
- Grant note
- R01MH072034 / NATIONAL INSTITUTE OF MENTAL HEALTH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000255611200001
- Scopus ID
- 2-s2.0-43949140878
- Other Identifier
- 991019320714404721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic