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EEG signal Analysis using Decomposed Wavelet Transform and Fuzzy Inference System

Authors: Mohammad A Obeidat and Ayman M Mansour

The recognition of an abnormal activity of the brain functionality is a vital issue. To determine the type of the abnormal activity either a brain image or brain signal are usually considered. Imaging localizes the defect within the brain area and relates this area with some body functionalities. However, some functions may be disturbed without affecting the brain. In this case, imaging may not provide the symptoms of the problem. A cheaper yet efficient approach that can be utilized to detect abnormal activity is the measurement and analysis of the electroencephalogram (EEG) signals. The main goal of this work is to come up with a new method to facilitate the classification of the abnormal and disorder activities within the brain directly using wavelet transform and Fuzzy Inference System, which makes it possible to be applied in an on-line monitoring system. Wavelet depends on transforming the EEG signal into another domain to easily extract significant features that allow proper classification. Fuzzy rule based approach has been used in this paper with Wavelet to classify EEG signal as normal or abnormal. Through inference Engines and membership functions, the fuzzy terms abstracted from decomposed wavelet of EEG will be inferred to the corresponding fuzzy semantic presentations. Using real patient data, the proposed approached was tested. An experienced physician worked with our team evaluated the computer results and decided that the system doesn’t miss any case of the tested cases.

EEG, Fuzzy System, Wavelet Transform, Inference System, Classifier.

Volume 22, Issue 1, October 2014, pp. 918-924                    Download PDF


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