BrainTwin-AI: A Multimodal MRI-EEG-Based Cognitive Digital Twin for Real-Time Brain Health Intelligence.

Overview

1. Department of Computer Science, SNEC, University of Calcutta, Kolkata 700073, West Bengal, India
2. Department of Computer Science and Engineering (Specialization in Internet of Things), Institute of Engineering and Management, Kolkata 700091, West Bengal, India
3. Department of Computer Science and Engineering, University of Engineering and Management, Kolkata 700160, West Bengal, India
4. Department of Computer Science, Illinois Institute of Technology, Chicago, IL 60616, USA
5. Department of Computer Science, San Francisco State University, San Francisco, CA 94132, USA

Abstract

Background/Objectives: Brain health monitoring is increasingly essential as modern cognitive load, stress, and lifestyle pressures contribute to widespread neural instability. The paper presents BrainTwin, a next-generation cognitive digital twin, as a patient-specific, constantly updating computer model that combines state-of-the-art MRI analytics for neuro-oncological assessment related to clinical study and management of tumors affecting the central nervous system (including their detection, progression, and monitoring) with real-time EEG-based brain health intelligence. Methods: 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. The 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 brainwave analysis, while a BiLSTM classifier distinguishes relaxed, stress, and fatigue states, which are probabilistically inferred cognitive conditions derived from EEG spectral patterns. Unlike static MRI imaging, EEG provides real-time brain health monitoring. The BrainTwin performs EEG–MRI fusion, correlating 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. Results: The evaluation metrics demonstrate a BiLSTM macro-F1 of 0.94 (Precision/Recall/F1: Relaxed 0.96, Stress 0.93, Fatigue 0.92) and a ViT++ MRI accuracy of 96%, outperforming baseline architectures. Conclusions: These results demonstrate BrainTwin’s reliability, interpretability, and clinical utility as an integrated digital companion for tumor assessment and real-time functional brain monitoring.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data Availability Statement

The in-house EEG and MRI datasets generated and analyzed during the current study are not publicly available due to ethical and privacy considerations. External validation datasets used in this work, including BRaTS 2021 (MRI) and the TUH EEG Corpus, are publicly accessible. Detailed algorithmic implementations can be accessed through: https://github.com/UtshoBanerjee/BrainTwin (accessed on 19 January 2026). Additional anonymized data may be made available from the corresponding author upon reasonable request and subject to institutional approval.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 keywords, 20 references.

Cite

This paper

Saha, H. N., Banerjee, U., Karmakar, R., Banerjee, S., & Turdiev, J. (2026). BrainTwin-AI: A Multimodal MRI-EEG-Based Cognitive Digital Twin for Real-Time Brain Health Intelligence. Brain sciences, 16(4), 411. https://doi.org/10.3390/brainsci16040411

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