- This thesis presents a control metric designed to create clinically interpretable, high–signal quality recordings with water-based EEG, utilizing impedance and zero-crossings
- The metric was integrated into a newly developed, intuitive, user-centred interface for independent patient use in a home environment.
- A home study using this interface demonstrated that it enabled independent use. Approximately half of the recordings were clinically interpretable, the other half were not, primarily due to low-voltage signals. Signal quality was lower than in previous studies conducted under technical supervision.
- The metric requires further optimization to improve signal quality and clinical interpretability, with strict electrode-specific impedance (<100 kΩ) and low-voltage signal handling.
- In a controlled environment, prolonged water-based EEG recordings of up to three hours were feasible, with stable signal quality and acceptable user comfort.
- The usability of the proposed solution should be further validated in people with epilepsy. Its potential role between wearable solutions and inpatient monitoring for therapeutic applications should be further established.

Generalized epilepsy is a common neurological disorder characterised by sudden abnormal brain activity, called a seizure, leading to uncontrolled physical conditions like staring, jerking, stiffening, or loss of consciousness, resulting in seizure-related accidents. These seizures are managed and treated with drugs. The anti-seizure drugs are developed by preclinical and clinical studies involving analysis of Local Field Potential (LFP) recordings from the brain by identifying biomarkers for the development of epilepsy. The analysis is usually done manually, making it a time-consuming process. Automating the manual process is challenging as the LFP recordings are nuanced and require temporal context for seizure detection and analysis due to the variable morphology of seizures in different subjects. To effectively capture these nuances and model long-term dependencies in LFP signals, the application of Large Language Models (LLMs) is an emerging focus of research and development. Hence, as a contribution to this advancing field of research, we present an explainable Generative AI framework, EpiLiteGPT. We present a pipeline under this framework that detects generalized seizures in a Dravet Syndrome mouse model. The pipeline classifies intracranial EEG (iEEG) segments as normal, artefact, or seizure, and the Seizure Insight Module (SIM) then carries out seizure detection based on these classifications. Furthermore, the pipeline is optimized for hardware resource efficiency, gearing towards potential real-time edge deployment. The pipeline achieves an average seizure event detection sensitivity of 81.5% across 6 subjects (2 Training Mice, 4 Held-Out Mice), while the hardware optimizations reduce the energy consumption by 85%, speed up the pipeline by 2.86x and reduce the LLM memory footprint by 75% from the baseline implementation. Fundamentally, the pipeline reduces time required for seizure detection by 97% as compared to manual analysis, thereby accelerating epilepsy research. Additionally, the proposed framework introduces a novel curriculum learning strategy for training LLMs on iEEG signals and develops a GPT-2-based backbone with potential for the development of personalised seizure detection devices. In doing so, it contributes to the growing body of research on LLMs for seizure detection and their prospective deployment on edge devices.