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Artificial Neural Networks in EEG Analysis

Artificial Neural Networks in EEG Analysis

Markad V. Kamath, Adrian R. Upton, Jie Wu, Harjeet S. Bajaj, Skip Poehlman, Robert Spaziani
Copyright: © 2006 |Pages: 18
ISBN13: 9781591408482|ISBN10: 1591408482|ISBN13 Softcover: 9781591408499|EISBN13: 9781591408505
DOI: 10.4018/978-1-59140-848-2.ch008
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MLA

Kamath, Markad V., et al. "Artificial Neural Networks in EEG Analysis." Neural Networks in Healthcare: Potential and Challenges, edited by Rezaul Begg, et al., IGI Global, 2006, pp. 177-194. https://doi.org/10.4018/978-1-59140-848-2.ch008

APA

Kamath, M. V., Upton, A. R., Wu, J., Bajaj, H. S., Poehlman, S., & Spaziani, R. (2006). Artificial Neural Networks in EEG Analysis. In R. Begg, J. Kamruzzaman, & R. Sarker (Eds.), Neural Networks in Healthcare: Potential and Challenges (pp. 177-194). IGI Global. https://doi.org/10.4018/978-1-59140-848-2.ch008

Chicago

Kamath, Markad V., et al. "Artificial Neural Networks in EEG Analysis." In Neural Networks in Healthcare: Potential and Challenges, edited by Rezaul Begg, Joarder Kamruzzaman, and Ruhul Sarker, 177-194. Hershey, PA: IGI Global, 2006. https://doi.org/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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