Detection of Brain Tumor in EEG Signals Using Independent Component Analysis

Abstract

The Electroencephalogram(EEG) is Scientifically becoming an important tool of measuring brain activity. The EEG data is used to diagnose brain diseases and brain abnormalities. EEG helps to suit the increasing demand of brain tumor detection on affordable prices with better clinical and healthcare services. This research work presents a technique of efficient brain tumor detection in EEG signals using Independent Component Analysis(ICA). EEG signals which actually are carrying information regarding brain abnormalities are also contaminated by the artefacts both from subjects and equipment interferences. Artefacts are removed using adaptive filtering techniques(ICA). The signal features are extracted by ICA which are buried in  wide noise band. This clean artefact free EEG signal is then used as a train input for Maximum Likelihood Detector.  The trained input is then fed with test EEG signals. This way the presence of brain tumor in EEG signal is effectively detected. The results obtained experimentally demonstrate the efficiency of the technique in removing artefacts from EEG signals for efficient of brain tumor detection.

Keywords

• Elimination; Electroencephalogram; Image processing; Diagnose; Brain; abnormalities; Detection; Signals Detector;

References

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