BrainTwin.AI: A New-Age Cognitive Digital Twin Advancing MRI-Based Tumor Detection and Progression Modelling via an Enhanced Vision Transformer, Powered with EEG-Based Real-Time Brain Health Intelligence

Brain health monitoring is increasingly essential as modern cognitive load, stress, and lifestyle pressures contribute to widespread neural instability. The paper introduces BrainTwin, a next-generation cognitive digital twin that integrates advanced MRI analytics for comprehensive neuro-oncological assessment with real-time EEG–based brain health intelligence.Structural analysis is driven by an Enhanced Vision Transformer (ViT++), which improves spatial representation and boundary localization, achieving more accurate tumor prediction than conventional models. Extracted tumor volume forms the baseline for short-horizon tumor progression modeling. Parallel to MRI analysis, continuous EEG signals are captured through an in-house wearable skullcap, preprocessed using Edge AI on a Hailo Toolkit–enabled Raspberry Pi 5 for low-latency denoising and secure cloud transmission. Pre-processed EEG packets are authenticated at the fog layer ensuring secure and reliable cloud transfer, enabling significant load reduction in the edge and cloud nodes. In the digital twin, EEG characteristics offer real-time functional monitoring through dynamic brain-wave analysis,while a BiLSTM classifier distinguishes relaxed, stress, and fatigue states. Unlike static MRI imaging, EEG provides real-time brain health monitoring. The Brain-Twin performs EEG–MRI fusion, co-relating functional EEG metrics with ViT++ structural embeddings to produce a single risk score that can be interpreted by clinicians to determine brain vulnerability to future diseases. Explainable artificial intelligence (XAI) provides clinical interpretability through Gradient weighted class activation mapping (Grad-CAM) heatmaps, which are used to interpret ViT++ decisions and are visualized on a 3D interactive brain model to allow more in-depth inspection of spatial details. The evaluation metrics demonstrate a BiLSTM macro-F1 of 0.94 (Precision/ Recall/ F1: Relaxed 0.96, Stress 0.93, Fatigue 0.92) and ViT++ MRI accuracy of 96% outperforming baseline architectures. These results demonstrate BrainTwin’s reliability, interpretability, and clinical utility as an integrated digital companion for tumor assessment and real-time functional brain monitoring.