Volume : III, Issue : VII, July - 2014

Classification of EEG Spectrogram using Adaptive Resonance Theory–2

Bindu. R, Dr S G Hiremath, Shilpa Biradar

Abstract :

Electroencephalography (EEG) is a well Known tool to capture the human ain signals. This paper uses the time frequency approach known as spectrogram image processing technique for analyzing the EEG signal, which was generated by the technique Short Time Fourier Transform. The features were extracted by using the Gray Level Cooccurrence Matrix (GLCM). The four feature mean, variance, standard deviation and range were extracted using the Gray Level Cooccurrence Matrix (GLCM). The extracted features were given as the input to the Adaptive Resonance Theory-2 (ART2) classifier. Then the Adaptive Resonance Theory-2 (ART2) classifier was employed to classify the EEG spectrogram image. The results showed that the Adaptive Resonance Theory-2 (ART2) classifier was able to EEG spectrogram image with accuracy of 96%.

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Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

Bindu.R, Dr S G Hiremath, Shilpa Biradar Classification of Eeg Spectrogram using Adaptive Resonance Theory-2 International Journal of Scientific Research, Vol : 3, Issue : 7 July 2014

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