DS003775 • SRM Resting-state EEG Dataset

NeuroAge AI

Explainable Deep Learning Framework for EEG-Based Prediction
and Visualization of Accelerated Brain Aging

111 Subjects
25 EEG Features
10 ML Models
SHAP Explainability
Explore Knowledge Hub Start Prediction
Neuro-Science Academy

🧠 Neuro-Knowledge Hub

Master the fundamentals of EEG analysis and Brain Age Prediction

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Electronic Brainprint

Electroencephalography (EEG) records the brain's spontaneous electrical activity over time. It measures voltage fluctuations resulting from ionic current within the neurons of the brain.

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Neural Oscillations

Delta (<4 Hz): Dominant during deep sleep; associated with healing and recovery.
Theta (4-8 Hz): Linked to memory, intuition, and deep relaxation.
Alpha (8-13 Hz): The 'resting' brain; indicates calm focus and mental coordination.
Beta (13-30 Hz): Active thought, problem-solving, and logical focus.
Gamma (>30 Hz): Peak mental performance, high-level information processing.
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Accelerated Aging

As we age, neural networks naturally change. Accelerated Brain Aging occurs when functional markers (like Alpha power) decline faster than the chronological average, often signaling metabolic stress or cognitive risk.

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Explainable Deep Learning

Our framework uses an Ensemble of 10 Models, including Deep Neural Networks (MLP), to analyze 25 regional power features. SHAP values decompose these complex decisions into human-readable visual contributions.

📊 Subject Input

Enter pre-extracted EEG spectral power features or load a sample subject from the dataset

Quick Load:

🤖 Select Prediction Model

Optional — enables brain age gap calculation
EEG Spectral Features