Advances in the Application of Brain Computer Interface in Seizure Detection and Prediction and Epilepsy Treatment

Epilepsy is a prevalent neurological condition impacting nearly fifty million individuals around the world. Traditional diagnosis based on clinical symptoms and neural monitoring can be inefficient or inaccurate. Pharmacotherapy is the first-line treatment, but about one-third of patients are drug-resistant and may experience adverse effects. Surgical resection is another option but is not suitable for all patients. Brain-computer interfaces (BCIs) create a direct communication link between neural activity and external systems, enabling more effective automated seizure detection, forecasting, and individualized therapeutic interventions. Among the various BCI modalities, ranging from implanted electrodes to external sensors, non-invasive, EEG-based systems remain the most widely adopted due to their safety, cost-effectiveness, and ease of use. AI algorithms are used in BCI to process data and detect biomarkers like recently discovered high frequency oscillations (HFOs), brain connectivity, and microstates automatically before sending targeted stimulations or keeping track of the patients status remotely. Responsive neurostimulation (RNS) is a neuromodulation system that allows adaptive stimulation, meaning that it is closed-loop, which has the potential of minimizing side effects. This review aims at discussing and evaluating the effectiveness of BCI in seizure detection, prediction, and patient-specific treatments while providing enlightenment on future trends.