Computational intelligence in multi-channel EEG signal analysis

Abstract

Computational intelligence and signal analysis of multi-channel data form an interdisciplinary research area based upon general digital signal processing methods and adaptive algorithms. The chapter is restricted to their use in biomedicine and particularly in electroencephalogram signal processing to find specific components of such multi-channel signals. Methods presented include signal de-noising, evaluation of their fundamental components and segmentation based upon feature detection in time-frequency and time-scale domains using both the discrete Fourier transform and the discrete wavelet transform. Resulting pattern vectors are then classified by self-organizing neural networks 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 segments features. Proposed methods verified in the MATLAB environment using distributed data processing are accompanied by the appropriate graphical user interface that enables convenient and user friendly time-series processing.