Rohira, VridhiVridhiRohiraChaudhary, ShivamShivamChaudharyDas, SudipSudipDasPrasad Miyapuram, KrishnaKrishnaPrasad Miyapuram2025-08-312025-08-312023-01-04[9781450397988]10.1145/3570991.35709952-s2.0-85146144359https://d8.irins.org/handle/IITG2025/26929Epilepsy is a neurological condition characterized by recurrent seizures and affects millions of people all over the world. The abnormal brain electrical activity during an epileptic seizure can be seen with an EEG, which is then read by a trained medical professional to diagnose epilepsy. However, this is often time-consuming, expensive, inaccessible, and inaccurate, thus highlighting the need for automated epilepsy prediction. Previous algorithms for this problem only made use of small data sets which lacked variable, clinical grade data. We used the TUEP dataset to extract features through power spectral density and power spectral connectivity. These features were then classified into epileptic vs non-epileptic using a random forest classifier. Our feature extraction methods using power spectral density and spectral connectivity showed accuracies of over 90% in detecting epilepsy.falseEEG | Epilepsy | Random forest classifierConference Paper272-2734 January 20234cpConference Proceeding5