Automated characterization of multiple alpha peaks in multi-site electroencephalograms

被引:40
作者
Chiang, A. K. I. [1 ,2 ,3 ]
Rennie, C. J. [1 ,2 ,3 ,4 ]
Robinson, P. A. [1 ,2 ,3 ,5 ]
Roberts, J. A. [1 ,2 ,3 ,6 ,7 ]
Rigozzi, M. K. [1 ,6 ,7 ]
Whitehouse, R. W. [1 ]
Harnilton, R. J. [6 ,7 ]
Gordon, E. [6 ,7 ,8 ]
机构
[1] Univ Sydney, Sch Phys, Sydney, NSW 2006, Australia
[2] Univ Sydney, Brain Dynam Ctr, Westmead Millennium Inst, Westmead Hosp, Westmead, NSW 2145, Australia
[3] Univ Sydney, Western Clin Sch, Westmead, NSW 2145, Australia
[4] Univ Sydney, Westmead Hosp, Dept Med Phys, Westmead, NSW 2145, Australia
[5] Univ Sydney, Fac Med, Sydney, NSW 2006, Australia
[6] Brain Resource Int Database, Ultimo, NSW 2007, Australia
[7] Brain Resource Co, Ultimo, NSW 2007, Australia
[8] Univ Sydney, Dept Psychol Med, Sydney, NSW 2006, Australia
基金
澳大利亚研究理事会;
关键词
EEG; alpha rhythm; automatic detection;
D O I
10.1016/j.jneumeth.2007.11.001
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The identification of alpha rhythm in the human electroencephalogram (EEG) is generally a laborious task involving visual inspection of the spectrum. Moreover the occurrence of multiple alpha rhythms is often overlooked. This paper seeks to automate the process of identifying alpha peaks and quantifying their frequency, amplitude and width as a function of position on the scalp. Experimental EEG was fitted with parameterized spectra spanning the alpha range, with results categorized by multi-site criteria into three distinct classes: no distinguishable alpha peak, a single alpha peak, and two alpha peaks. The technique avoids visual bias, integrates spatial information, and is automated. We show that multiple alpha peaks are a common feature of many spectra. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:396 / 411
页数:16
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