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Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data
Sencer Melih Deniz1,2, Ahmet Ademoglu1, Adil Deniz Duru3
1Institute of Biomedical Engineering, Bogazici University, Istanbul 34684, Turkey.
Brain Sciences
|July 29, 2025
Summary
This study effectively differentiates emotional and cognitive states using electroencephalography (EEG) and graph theory. Graph-theoretic metrics accurately classify moods and cognitive load, highlighting EEG
Area of Science:
- Neuroscience
- Cognitive Science
- Signal Processing
Background:
- Emotion and cognition are key human mental processes, often studied separately.
- Physiological measurements, particularly electroencephalography (EEG), offer objective insights into these states.
- Event-related potentials (ERPs) analyzed via EEG provide detailed temporal and spatial information.
Purpose of the Study:
- To discriminate between pleasant/unpleasant emotional moods.
- To differentiate between low/high cognitive states.
- To evaluate the efficacy of graph-theoretic features from spatio-temporal EEG components for classification.
Main Methods:
- Collected emotional and cognitive data using electroencephalography (EEG).
- Analyzed wavelet coherence of single-trial ERP components (N100, N200, P300) across delta, theta, alpha, and beta bands.
- Applied graph-theoretic analyses to connectivity maps and used metrics (e.g., efficiency, clustering coefficient) for classification with SVM, K-NN, and LDA.
Main Results:
- Achieved high classification accuracy for emotional states (up to 92%) and cognitive states (up to 89%).
- Demonstrated the effectiveness of graph-theoretic metrics derived from wavelet coherence.
- Identified delta band ERP components as particularly informative.
Conclusions:
- Graph-theoretic metrics derived from wavelet coherence of delta band ERP components, when used with Support Vector Machines (SVM), can accurately discriminate emotional and cognitive states.
- This approach offers a reliable, objective method for assessing mental states.
- Highlights the potential of advanced signal processing techniques in understanding brain function.
