A Novel Approach for Extracting Nociceptive-Related Time-Frequency Features in Event-Related Potentials

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)

Abstract

In the present study, we are mainly objective to develop a novel approach to extract nociceptive-related features in the time-frequency domain from the event-related potentials (ERPs) that were recorded using high-density EEG. First, the independent component analysis (ICA) was used to separate single-trial ERPs into a set of independent components (ICs), which were then clustered into three groups (symmetrically distributed ICs, non-symmetrically distributed ICs, and noise-related ICs). Second, the time-frequency distributions of each clustered group were calculated using continuous wavelet transform (CWT). Third, the principal component analysis (PCA) with varimax rotation was used to extract time-frequency features from all single-trial time-frequency distributions across all channels. Altogether, the developed approach would help effectively extracting nociceptive-related time-frequency features, thus yielding to an important contribution to the study of nociceptive-specific neural activities.

Keywords

Independent Component Analysis Varimax Rotation Independent Component Analysis Continuous Wavelet Transform Laser Stimulus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Orthopaedics and TraumatologyThe University of Hong KongHong KongChina
  2. 2.China Key Laboratory of Cognition and Personality and School of PsychologySouthwest UniversityChongqingChina

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