EEG Signal Analysis: A Survey

被引：394

作者：

Subha, D. Puthankattil ^{[2
]}

Joseph, Paul K. ^{[2
]}

Acharya U, Rajendra ^{[1
]}

Lim, Choo Min ^{[1
]}

机构：

[1] Ngee Ann Polytech, Dept Elect & Comp Engn, Singapore, Singapore

[2] Natl Inst Technol, Dept Elect Engn, Calicut, Kerala, India

来源：

JOURNAL OF MEDICAL SYSTEMS | 2010年 / 34卷 / 02期

关键词：

EEG; Correlation dimension; Fractal dimension; Entropy; Recurrence plot; NONLINEAR-ANALYSIS; TIME-SERIES; APPROXIMATE ENTROPY; WAVELET TRANSFORM; PHASE SYNCHRONY; SLEEP; DIMENSION; ELECTROENCEPHALOGRAM; RECOGNITION; MEDITATION;

D O I：

10.1007/s10916-008-9231-z

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.

引用

页码：195 / 212

页数：18

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