DENOISING & FEATURE EXTRACTION OF EEG SIGNAL USING WAVELET TRANSFORM

摘要

Brain is one of the most complex organ of the humans, it controls the coordination of human muscles & nerves. EEG keeps its importance for identifying the physiological, and the psychological situations of the human and the functional activity of the brain. Being a non stationary signal, suitable analysis is essential for EEG to differentiate the normal EEG and epileptic seizures. Epilepsy is one of the most common neurological disorders. Epilepsy is a recurrent seizure disorder caused by abnormal electrical discharges from the brain cells, often in the cerebral cortex. Feature extraction of EEG signals is core issues on EEG based brain mapping analysis. This paper proposes classification system for epilepsy based on neural networks and wavelet based feature extraction technique has been adopted to extract features Min, Max, Mean and Median. These features have been applied to Neural Networks for classification. The results gave a classification accuracy of 97%.