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Computational Intelligence and Neuroscience
Volume 2007, Article ID 10479, 24 pages
Research Article

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

1Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi 351-0198, Saitama, Japan
2Neuroscience Statistics Research Lab, Massachusetts General Hospital, Harvard Medical School, Boston 02114, MA, USA
3Department of Neurosurgery, Johns Hopkins Hospital, Baltimore 21287-7274, MD, USA
4Department of Neurosurgery, Kyoto Kizugawa Hospital 26-1, Nishi-Rokutan, Hirakawa Joyo 610-0101, Kyoto, Japan
5Department of Electronics and Information Engineering, Saitama Institute of Technology, Fukaya-shi 369-0293, Saitama, Japan

Received 25 December 2006; Accepted 18 May 2007

Academic Editor: Saied Sanei

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.


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.