Imagine a world where diagnosing neurological disorders is faster, more accessible, and incredibly precise. Researchers have developed a groundbreaking hybrid AI system that automatically analyzes EEG brain wave data and generates comprehensive reports, potentially revolutionizing how we diagnose conditions like epilepsy, dementia, and brain lesions. This system tackles the critical problem of EEG misinterpretation, a challenge particularly prevalent in smaller hospitals and clinics lacking specialized expertise. The core of this innovation lies in a hybrid approach. The system uses deep learning to predict the Posterior Dominant Rhythm (PDR), a key indicator of brain activity. Think of it as an AI that learns to recognize healthy brain wave patterns. It achieves remarkable accuracy, outperforming traditional methods by a significant margin. This AI learns from a dataset of EEGs labeled by expert neurologists, allowing it to predict PDR with an accuracy of 91.8% within a tight error margin. But accurate brain wave analysis can be like finding a needle in a haystack, as EEG data is often riddled with 'noise' – artifacts from eye movements, muscle activity, or even electromagnetic interference. This AI system cleverly uses an unsupervised artifact removal method. It automatically identifies and cleans up these artifacts without needing manual intervention, ensuring cleaner data for analysis. Combining this with expert-designed algorithms, the AI accurately analyzes the EEG background for abnormalities, aiding in the early detection of various neurological conditions. The innovation doesn't stop at analysis. This system goes a step further by using large language models (LLMs) – the technology behind AI chatbots – to generate detailed, human-readable EEG reports. These reports were validated by an independent panel of experts, confirming their impressive 100% accuracy. This innovation could significantly reduce the workload on neurologists and ensure consistent reporting across different healthcare settings. While this research primarily used data from a single center, the AI system was validated on a public dataset, demonstrating its potential for wider application. Future work aims to incorporate data from multiple centers and expand the system’s capabilities to diagnose a broader range of neurological disorders. This hybrid AI system represents a significant leap forward in EEG analysis, promising a future of faster, more accurate neurological diagnoses, especially for those with limited access to specialized healthcare.