Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

Electroencephalogram (EEG) signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a...

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Bibliographic Details
Main Authors: E. Parvinnia, M. Sabeti, M. Zolghadri Jahromi, R. Boostani
Format: Article
Language:English
Published: Elsevier 2014-01-01
Series:Journal of King Saud University: Computer and Information Sciences
Subjects:
Nearest neighbor
Noisy training data
EEG signal classification
Band power
Fractal dimension
Autoregressive coefficients
Online Access:http://www.sciencedirect.com/science/article/pii/S1319157813000025

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http://www.sciencedirect.com/science/article/pii/S1319157813000025

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