EEG Signals: Performance Analysis of the Firefly Optimization Technique for Classifying Epilepsy Risk Levels

The analysis of Electro Encephalography (EEG) signals is crucial in the domains of brain engineering and neuroscience, as it provides insights into the brain’s electrical activity. In this study, the risk of epilepsy was assessed using the Firefly method for feature selection; with the ShortTerm Fourier Transform (STFT) being used to extract the necessary features from the bio signals. The STFT method was found to be effective in extracting parameter components and comprehending important data related to brain function. The Firefly optimization technique was used for feature selection, and the classification was performed using the reduced dimensions. The results were compared against those of traditional machine learning methods. The proposed methodology was tested on various Bio signal datasets, with the epileptic and Bonn datasets achieving a classification accuracy of 97.5% and 95.31%, respectively