Abstract:

The elimination of artefacts from Electroencephalogram(EEG) has an important role many signal and image processing applications. The artefacts are the noises that appears during the acquisition of signals from the patient body. With the presence of these artefacts it become difficult for doctors and technicians to analyse the Electroencephalogram signals efficiently. The aim of this research work is to remove these artefacts using Independent Components Analysis(ICA). The scalp EEG is intensively used as an important clinical tool for diagnosis and treatment of diseases. The probabilistic modified ICA algorithm is used to separate EEG signals from artifacts for efficiently brain tumor detection. This research work aims to detect epileptic activity for an electroencephalogram having sixteen-channels. The research consists of three important stages First one is data collection from patients, second is feature extraction and third one is EEG signal analysis. In feature extraction the stress is to detect epileptic form of activity  from the patient collected signals. In signal analysis stage the stress is to get information about the type of the brain tumor.

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