Logo image
Back
Extraction of Bistable-Percept-Related Features From Local Field Potential by Integration of Local Regression and Common Spatial Patterns
Journal article   Open access

Extraction of Bistable-Percept-Related Features From Local Field Potential by Integration of Local Regression and Common Spatial Patterns

Zhisong Wang, Alexander Maier, Nikos K Logothetis and Hualou Liang
IEEE transactions on biomedical engineering, v 56(8), pp 2095-2103
Aug 2009
DOI: https://doi.org/10.1109/TBME.2009.2018630
PMID: 19362902
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1109/TBME.2009.2018630View
Published, Version of Record (VoR) Open

Abstract

local regression Smoothing methods Scattering support vector machine (SVM) event-related synchronization and desynchronization single trial locally weighted scatterplot smoothing (LOWESS) Decoding Data mining stimulus-evoked activity Visual perception Support vector machines nonstationary time series structure-from-motion (SFM) Support vector machine classification Common spatial patterns (CSPs) Feature extraction Spatial filters local field potential (LFP) Signal design
Bistable perception arises when an ambiguous stimulus under continuous view is perceived as an alternation of two mutually exclusive states. Such a stimulus provides a unique opportunity for understanding the neural basis of visual perception because it dissociates the perception from the visual input. In this paper, we focus on extracting the percept-related features from the local field potential (LFP) in monkey visual cortex for decoding its bistable structure-from-motion (SFM) perception. Our proposed feature extraction approach consists of two stages. First, we estimate and remove from each LFP trial the nonpercept-related stimulus-evoked activity via a local regression method called the locally weighted scatterplot smoothing because of the dissociation between the perception and the stimulus in our experimental paradigm. Second, we use the common spatial patterns approach to design spatial filters based on the residue signals of multiple channels to extract the percept-related features. We exploit a support vector machine (SVM) classifier on the extracted features to decode the reported perception on a single-trial basis. We apply the proposed approach to the multichannel intracortical LFP data collected from the middle temporal (MT) visual cortex in a macaque monkey performing an SFM task. We demonstrate that our approach is effective in extracting the discriminative features of the percept-related activity from LFP and achieves excellent decoding performance. We also find that the enhanced gamma band synchronization and reduced alpha and beta band desynchronization may be the underpinnings of the percept-related activity.

Metrics

11 Record Views
4 citations in Web of Science
7 citations in Scopus

Details

UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Industry collaboration
Domestic collaboration
International collaboration
Web of Science research areas
Engineering, Biomedical
Logo image