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Artificial Neural Networks in EEG Analysis
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Author(s): Markad V. Kamath (McMaster University, Canada), Adrian R. Upton (McMaster University, Canada), Jie Wu (McMaster University, Canada), Harjeet S. Bajaj (McMaster University, Canada), Skip Poehlman (McMaster University, Canada)and Robert Spaziani (McMaster University, Canada)
Copyright: 2006
Pages: 18
Source title:
Neural Networks in Healthcare: Potential and Challenges
Source Author(s)/Editor(s): Rezaul Begg (Victoria University, Australia), Joarder Kamruzzaman (Monash University, Australia)and Ruhul Sarker (University of New South Wales, Australia)
DOI: 10.4018/978-1-59140-848-2.ch008
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Abstract
The artificial neural networks (ANNs) are regularly employed in EEG signal processing because of their effectiveness as pattern classifiers. In this chapter, four specific applications will be studied: On a day to day basis, ANNs can assist in identifying abnormal EEG activity in patients with neurological diseases such as epilepsy, Huntington’s disease, and Alzheimer’s disease. The ANNs can reduce the time taken for interpretation of physiological signals such as EEG, respiration, and ECG recorded during sleep. During an invasive surgical procedure, the ANNs can provide objective parameters derived from the EEG to help determine the depth of anesthesia. The ANNs have made significant contributions toward extracting embedded signals within the EEG which can be used to control external devices. This rapidly developing field, which is called brain-computer interface, has a large number of applications in empowering handicapped individuals to independently operate appliances, neuroprosthesis, or orthosis.
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