Biomedical Engineering Reference
In-Depth Information
CHAPTER 3
Single-Channel EEG Analysis
Hasan Al-Nashash
1
Shivkumar Sabesan, Balu Krishnan, Jobi S. George, Konstantinos Tsakalis, and
Leon Iasemidis
2
Shanbao Tong
3
In this chapter, we review the most commonly used quantitative EEG analysis meth-
ods for single-channel EEG signals, including linear methods, nonlinear descriptors,
and statistics measures: (1) In the linear methods section, we cover conventional
spectral analysis methods for stationary signals and the time-frequency distribution
property when the EEG is regarded as a nonstationary process; (2) because EEGs
have been regarded as nonlinear signals in past years, we also introduce the methods
of higher-order statistic (HOS) analysis and nonlinear dynamics in quantitative
EEG (qEEG) analysis; and, finally, (3) information theory is introduced to qEEG
measurements from the aspect of the randomness in EEG signals.
3.1
Linear Analysis of EEGs
An electroencephalograph is a record of the electrical activity generated by a large
number of neurons in the brain. It is recorded using surface electrodes attached to
the scalp or subdurally or in the cerebral cortex. The amplitude of a human surface
EEG signal is in the range of 10 to 100
V. The frequency range of the EEG has a
fuzzy lower and upper limit, but the most important frequencies from the physio-
logical viewpoint lie in the range of 0.1 to 30 Hz. The standard EEG clinical bands
are the delta (0.1 to 3.5 Hz), theta (4 to 7.5 Hz), alpha (8 to 13 Hz), and beta (14 to
30 Hz) bands [1, 2]. EEG signals with frequencies greater than 30 Hz are called
gamma waves and have been found in the cerebellar structures of animals [3, 4]. An
EEG signal may be considered a random signal generated by a stochastic process
and can be represented after digitization as a sequence of time samples [5-9].
EEG signal analysis is helpful in various clinical applications including predict-
ing epileptic seizures, classifying sleep stages, measuring depth of anesthesia, detec-
tion and monitoring of brain injury, and detecting abnormal brain states [10-23].
The alpha wave, for example, is observed to be reduced in children and in the
elderly, and in patients with dementia, schizophrenia, stroke, and epilepsy [24-26].
μ
1.
This author contributed to Section 3.1.
2.
These authors contributed to Section 3.2. The work presented in Section 3.2 was supported in part by the
American Epilepsy Research Foundation and the Ali Paris Fund for LKS Research and Education, and NSF
Grant ECS-0601740.
3.
This author contributed to Section 3.3. The work presented in Section 3.3 was supported in part by
Shuguang Program of the Education Commission of Shanghai Municipality.
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