Feature Extraction And Classification Of Eeg Signals Using Neural Network
Journal Title: International Journal of engineering Research and Applications - Year 2018, Vol 8, Issue 11
Abstract
The use of Electroencephalogram (EEG) or “brain waves” for human-computer interaction is a new and challenging field that has gained momentum in the past few years. In this work different finite impulse response filter (FIR) windowing techniques (Rectangular, Hamming, Hanning, Blackman, Kaiser β= 5,8,12) are used to extract EEG signal to its basic components (Delta wave, Theta wave, Alpha wave, Gamma and Beta wave).The comparison between these windowing methods are done by computing the Fourier transform, power spectrum, SNR values. The features are extracted from the data and applied to classification techniques to identify the accuracy in obtaining the information of the data. In this research, EEG from one subject who performed four tasks has been classified using Radial Basis Function (RBF) and Multi Layer Perceptron (MLP) neural networks. Five data sets with 1000 samples are chosen in order to perform classification techniques. 200 iterations are done to identify the best error rate. These iterations help us to achieve best output. We calculate the elapsed time, confusion matrix, sensitivity, precision, specificity and accuracy for the classified data. The best classification accuracy is approximately 99.66% using the Multi Layer Perceptron technique and the best windowing technique obtained is Kaiser β= 12. The experimental results are performed using MATLAB Tool.
Authors and Affiliations
choppalli Sruth, prof. P. Mallikarjuna Rao
Keywords
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- EP ID EP416594
- DOI 10.9790/9622-0811025360.
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