 This paper proposes a novel approach for distinguishing between focal and non-focal EEG signals. It uses entropy measures to analyze the complexity of the signals, which are then fed into a support vector machine, SVM, classifier to accurately identify the type of signal. The results demonstrate that the proposed method achieves an average classification accuracy of 87%, demonstrating its potential utility in clinical applications. This article was authored by Rajiv Sharma, Ram Bela's Pakori and Yuvrajendra Acharya.