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CONECT: Novel Weighted Networks Framework Leveraging Angle-Relation Connection (ARC) and Metaheuristic Algorithms for
Akashdeep Singh1, Supriya Supriya2, Siuly Siuly1
1Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC 3011, Australia.
Sensors (Basel, Switzerland)
|December 31, 2025
Summary
A new framework called CONECT transforms electroencephalography (EEG) signals into complex networks, improving dementia subtype classification by analyzing signal geometry. This method offers a more accurate and interpretable approach for diagnosing dementia. Keywords: dementia diagnosis, EEG analysis, CONECT framework, neuroscience.
Area of Science:
- Neuroscience
- Computational Biology
- Medical Imaging
Background:
- Dementia subtype classification using electroencephalography (EEG) is challenging.
- Traditional EEG analysis often overlooks crucial geometric and structural signal information.
- Existing methods may not fully capture the complexity of neural dynamics in dementia.
Purpose of the Study:
- Introduce CONECT (Complex Network Conversion and Topology), a novel framework for EEG analysis.
- Enhance the accuracy and interpretability of dementia subtype classification.
- Explore novel EEG biomarkers based on network topology and signal geometry.
Main Methods:
- Transformed EEG time series into weighted networks using a novel Angle-Relation Connection (ARC) rule.
- Developed a tunable edge-weighting function integrating amplitude, temporal, and angular components.
- Proposed new graph-based features: Weighted Angular Irregularity Index (WAII) and Curvature-Based Edge Feature Index (CBEFI).
- Applied Ant Colony Optimization (ACO) for feature selection on the OpenNeuro ds004504 dataset.
Main Results:
- The CONECT framework demonstrated potential for accurate dementia subtype classification.
- Novel graph-based features (WAII, CBEFI) showed promise as dementia biomarkers.
- Ant Colony Optimization improved classification performance and model transparency.
- The geometry-informed approach captured localized irregularity and signal geometry effectively.
Conclusions:
- CONECT offers a promising, interpretable, and geometry-informed framework for dementia diagnosis.
- The novel network-based approach advances EEG signal analysis in neuroscience.
- This method has the potential for practical application in clinical settings for dementia subtype identification.
