Statistical modeling and analysis of laser-evoked potentials of electrocorticogram recordings from awake humans

Authors:
Zhe Chen;Shinji Ohara;Jianting Cao;François Vialatte;Fred A. Lenz;Andrzej Cichocki
Affiliations:
Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan and Neuroscience Statistics Research Lab., Massachusetts General Hospital, Harvard Medical ...;Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD and Department of Neurosurgery, Kyoto Kizugawa Hospital, Kyoto, Japan;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan and Department of Electronics and Information Engineering, Saitama Institute of Technology, ...;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan;Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shi, Saitama, Japan
Venue:
Computational Intelligence and Neuroscience - EEG/MEG Signal Processing
Year:
2007

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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.