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Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG

Sencer Melih Deniz1,2, Ahmet Ademoglu1, Adil Deniz Duru3

  • 1Institute of Biomedical Engineering, Bogazici University, Istanbul 34684, Turkey.

Brain Sciences
|July 29, 2025
PubMed
Summary
This summary is machine-generated.

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

Keywords:
EEGbrain–computer interfaceclassificationcognitionemotionevent-related potentialswavelet coherence

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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.