Abstract
The goal of much effort in recent years has been to provide a simplified interpretation of the electroencephalogram (EEG) for a variety of applications, including the diagnosis of neurological disorders and the intraoperative monitoring of anesthetic efficacy and cerebral ischemia. Although processed EEG variables have enjoyed limited success for specific applications, few acceptable standards have emerged. In part, this may be attributed to the fact that commonly usedsignal processing tools do not quantify all of the information available in the EEG. Power spectral analysis, for example, quantifies only power distribution as a function offrequency, ignoring phase information. It also makes the assumption that thesignal arises from alinear process, thereby ignoring potential interaction betweencomponents of the signal that are manifested asphase coupling, a common phenomenon in signals generated fromnonlinear sources such as the central nervous system (CNS). This tutorial describes bispectral analysis, a method of signal processing that quantifies the degree of phase coupling between the components of a signal such as the EEG. The basic theory underlying bispectral analysis is explained in detail, and information obtained from bispectral analysis is compared with that available from thepower spectrum. The concept of abispectral index is introduced. Finally, several model signals, as well as a representative clinical case, are analyzed using bispectral analysis, and the results are interpreted.
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Sigl, J.C., Chamoun, N.G. An introduction to bispectral analysis for the electroencephalogram. J Clin Monitor Comput 10, 392–404 (1994). https://doi.org/10.1007/BF01618421
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DOI: https://doi.org/10.1007/BF01618421