Epileptic zonal the EEG signals and MRI scans are used for accurately detecting theepileptic zone using Machine learning algorithm

The proposed work is an advanced machine learning techniques that aids in detecting epileptic zones using a combination of electroencephalography (EEG) and magnetic resonance imaging (MRI), emphasizing the integration of signal processing algorithms for improved accuracy. Various methodologies, including Independent Component Analysis (ICA), Multiple Signal Classification (MUSIC), and Adaptive Spatial Filters (beam formers), are evaluated to benchmark and enhance the detection of seizure onset zones within the brain. The research underscores the importance of robust data acquisition and preprocessing techniques to ensure high-quality signal processing, thereby improving the reliability of zonal detection. The proposed algorithm, demonstrates that EEG and MRI integration allow for better spatial and temporal localization of epileptic activities. The results indicate that techniques such as high-density EEG and adaptive spatial filtering provide detailed insights into brain function during seizure events, while ICA effectively isolates significant signal components, reducing artifacts and improving detection sensitivity to 95%.