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Title: Multi-channel EEG signal segmentation and feature extraction
Authors: Procházka, Aleš
Mudrová, Martina
Vyšata, Oldřich
Háva, Robert
Araujo, Carmen Paz Suarez 
UNESCO Clasification: 120304 Inteligencia artificial
Issue Date: 2010
Project: Diseño y Evaluación de Herramientas Computacionales Inteligentes de Ayuda Al Diagnostico y Pronostico Del Deterioro Cognitivo, Enfermedad de Alzheimer y Otras Demencias. Implantación en Telemedicina. 
Journal: Proceedings - IEEE International Conference on Intelligent Engineering Systems 
Conference: 14th International Conference on Intelligent Engineering Systems, INES 2010 
Abstract: Signal analysis of multi-channel data form a specific area of general digital signal processing methods. The paper is devoted to application of these methods for electroencephalogram (EEG) signal processing including signal de-noising, evaluation of its principal components and segmentation based upon feature detection both by the discrete wavelet transform (DWT) and discrete Fourier transform (DFT). The self-organizing neural networks are then used for pattern vectors classification using a specific statistical criterion proposed to evaluate distances of individual feature vector values from corresponding cluster centers. Results achieved are compared for different data sets and selected mathematical methods to detect and to classify signal segments features. Proposed methods are accompanied by the appropriate graphical user interface (GUI) designed in the MATLAB environment.
ISBN: 978-1-4244-7650-3
ISSN: 1543-9259
DOI: 10.1109/INES.2010.5483824
Source: INES 2010 - 14th International Conference on Intelligent Engineering Systems, Proceedings, p. 317-320, (Julio 2010)
Appears in Collections:Actas de congresos
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