This article is part of the series The Empirical Mode Decomposition and the Hilbert-Huang Transform.

Open Access Research Article

Single-Trial Classification of Bistable Perception by Integrating Empirical Mode Decomposition, Clustering, and Support Vector Machine

Zhisong Wang1, Alexander Maier2, Nikos K Logothetis3 and Hualou Liang1*

Author Affiliations

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 für biologische Kybernetik, Spemannstraße 38, 72076 Tübingen, Germany

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EURASIP Journal on Advances in Signal Processing 2008, 2008:592742  doi:10.1155/2008/592742


The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2008/1/592742


Received:23 August 2007
Revisions received:25 January 2008
Accepted:10 March 2008
Published:18 March 2008

© 2008 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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).

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