Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks
JVA '06 Proceedings of the IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing
Advanced Digital Signal Processing and Noise Reduction
Advanced Digital Signal Processing and Noise Reduction
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
A method for visualizing independent spatio-temporal patterns of brain activity
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Noise Covariance Properties in Dual-Tree Wavelet Decompositions
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
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.