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The EEG signal consist various frequency bands, which represents human activities like emotion, attention sleep stage etc. For the detection of epileptical seizures, it is required to perform classification on the basis of various EEG segments. This paper, presents performance analysis of gamma band in EEG signal using short-time fourier transform (STFT). It also gives comparison of various classification methods and achieves very good accuracy with some classification techniques. Analysis has been performed with following stages like STFT, extraction of gamma frequency band, statistical features extraction and finally applied to classifier. This paper deals with extraction of statistical features from obtained 2-Dimensional data using STFT and performed classification in high frequency band for epilepsy. Here, proposed Random Forest (RF) classifier achieved accuracy of 90%.
Sameer et al. (Fri,) studied this question.