EEG-X: An Integrated Framework for Automated Quantitative EEG Analysis in Epilepsy Diagnosis

Area of Science:

• Neuroscience and Artificial Intelligence
• Medical Signal Processing

Background:

• Interpreting electroencephalography (EEG) for epilepsy diagnosis is challenging due to patient variability impacting AI algorithm generalization.
• Complex spatio-temporal characteristics and long-range dependencies in EEG signals require sophisticated analysis methods.

Purpose of the Study:

• To develop a novel EEG decoding framework for improved seizure detection.
• To address the limitations of current AI algorithms in cross-subject generalization for epilepsy diagnosis.

Main Methods:

• Proposed a novel framework integrating a graph convolutional neural network (GCN) for spatial dependencies and a multiresolution transformer for temporal dynamics.
• Developed a seizure detection algorithm using this framework, validated on a large, multicenter, cross-subject dataset.
• Established EEG-X, a cloud-based collaborative platform integrating seizure detection, interictal epileptiform detection, and EEG source imaging.

Main Results:

• The novel seizure detection algorithm achieved 96.5% accuracy with a low false alarm rate of 1.61/h.
• Demonstrated superior performance across diverse patient populations in a cross-subject validation.
• The EEG-X framework offers collaborative annotation, multi-platform access, and continuous monitoring capabilities.

Conclusions:

• The proposed AI framework significantly enhances EEG-based seizure detection accuracy and generalization.
• The EEG-X platform facilitates practical implementation of quantitative EEG analysis for clinical and research applications.
• The study highlights the potential for AI-driven EEG analysis to improve epilepsy care and research efficiency.