Riemannian Procrustes Analysis in EEG-Based SPD-Net: Advancing Clinical AI for Brain Signal Classification and Healthcare AI Integration
Introduction: Why EEG AI Classification Matters Now Artificial intelligence is transforming modern medicine. From radiology automation to predictive analytics, hospitals now rely on intelligent systems to improve efficiency, reduce cost, and enhance patient outcomes. One of the most promising frontiers is EEG-based Clinical AI . Electroencephalography (EEG) captures brain electrical activity in real time. It is used in: Epilepsy detection Sleep disorder diagnosis Cognitive decline screening Depression and emotion analysis Stroke recovery monitoring Brain-computer interface (BCI) systems However, EEG signals are notoriously difficult to analyze. Every patient has unique brainwave patterns. Noise, session differences, and device variability often reduce model accuracy. That is where Riemannian Procrustes Analysis (RPA), combined with SPD-Net, creates a major opportunity. A recent study demonstrated that aligning EEG covariance matrices geometrically before deep learning can significant...