Towards Detection of Brain Tumor in Electroencephalogram Signals Using Support

researches. In this paper, we present an effective system for classification of electroencephalogram (EEG) signals that contain credible cases of brain tumor. The classification technique support vector machine is utilized in the proposed system for detecting brain tumors. In general, the EEG signals carry information about abnormalities or responses to certain stimulus in the human brain. However, EEG signals are highly contaminated with various artifacts, both from the subject and from equipment interferences. Initially, the artifacts present in the EEG signal are removed using adaptive filtering. Then the spectral analysis method is applied for extracting generic features embedded in an EEG signal. Precisely, Fast Fourier Transform for spectral analysis is used to separate the signal features which are buried in a wide band of noise. The radial basis function-support vector machine is trained using the clean EEG data obtained. With proper testing and training, we effectively classify the EEG signals with brain tumor. The key advent of the proposed approach is that it enables early detection of brain tumors initiating quicker clinical responses. Index Terms—Adaptive filtering, Artifacts, Brain, Brain tumor, Electroencephalogram (EEG), Electro-oculogram (EOG), Fast Fourier Transform (FFT), Seizure, Spectral analysis, Support vector machine.