Multi-channel EEG signal segmentation and feature extraction

Ales Prochazka, Martina Mudrova, Oldrich Vysata, Robert Hava, Carmen Paz Suarez Araujo
2010 2010 IEEE 14th International Conference on Intelligent Engineering Systems  
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
more » ... cific 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.
doi:10.1109/ines.2010.5483824 fatcat:o5huqvbea5cergzheffjeip524