Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience / 2007 / Article
Special Issue

EEG/MEG Signal Processing

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Research Article | Open Access

Volume 2007 |Article ID 010479 | https://doi.org/10.1155/2007/10479

Zhe Chen, Shinji Ohara, Jianting Cao, François Vialatte, Fred A. Lenz, Andrzej Cichocki, "Statistical Modeling and Analysis of Laser-Evoked Potentials of Electrocorticogram Recordings from Awake Humans", Computational Intelligence and Neuroscience, vol. 2007, Article ID 010479, 24 pages, 2007. https://doi.org/10.1155/2007/10479

Statistical Modeling and Analysis of Laser-Evoked Potentials of Electrocorticogram Recordings from Awake Humans

Academic Editor: Saied Sanei
Received25 Dec 2006
Accepted18 May 2007
Published05 Aug 2007

Abstract

This article is devoted to statistical modeling and analysis of electrocorticogram (ECoG) signals induced by painful cutaneous laser stimuli, which were recorded from implanted electrodes in awake humans. Specifically, with statistical tools of factor analysis and independent component analysis, the pain-induced laser-evoked potentials (LEPs) were extracted and investigated under different controlled conditions. With the help of wavelet analysis, quantitative and qualitative analyses were conducted regarding the LEPs' attributes of power, amplitude, and latency, in both averaging and single-trial experiments. Statistical hypothesis tests were also applied in various experimental setups. Experimental results reported herein also confirm previous findings in the neurophysiology literature. In addition, single-trial analysis has also revealed many new observations that might be interesting to the neuroscientists or clinical neurophysiologists. These promising results show convincing validation that advanced signal processing and statistical analysis may open new avenues for future studies of such ECoG or other relevant biomedical recordings.

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Copyright © 2007 Zhe Chen et al. 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.


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