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Emotion Detection from EEG Signals Using Machine Deep Learning Models.

João Vitor Marques Rabelo Fernandes1, Auzuir Ripardo de Alexandria1, João Alexandre Lobo Marques2

  • 1Programa de Pós-Graduação em Engenharia de Telecomunicações, Instituto Federal do Ceará (IFCE), Fortaleza 60040-215, Brazil.

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Summary
This summary is machine-generated.

Detecting emotions using electroencephalogram (EEG) signals is advancing. Graph Convolutional Neural Networks (GCNN) show promise for accurate emotion classification, outperforming other machine learning models in subject-dependent experiments.

Keywords:
deep learningelectroencephalogramemotion detectionemotion recognitiongraph convolutional neural networksmachine learning

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Area of Science:

  • Neuroscience and Artificial Intelligence
  • Computational Neuroscience
  • Affective Computing

Background:

  • Emotion detection is crucial for understanding human responses.
  • Electroencephalogram (EEG) offers a direct, non-invasive method for brain activity monitoring.
  • EEG-based emotion detection is valuable for brain-computer interfaces and mental health applications.

Purpose of the Study:

  • To evaluate machine learning and deep learning models for classifying emotions (positive, negative, neutral) from EEG signals.
  • To compare the performance of Graph Convolutional Neural Networks (GCNN) against other models like Deep Neural Networks (DNN) and Support Vector Machines (SVM).
  • To analyze the effectiveness of critical EEG signal attributes, including Differential Entropy (DE) and Power Spectral Density (PSD), for emotion classification.

Main Methods:

  • Utilized the public SEED (SJTU Emotion EEG Dataset) dataset, comprising EEG signals from participants watching emotional movie clips.
  • Employed a subject-dependent experimental approach for model evaluation.
  • Focused on Graph Convolutional Neural Networks (GCNN) and compared its performance with Deep Neural Networks (DNN) and Support Vector Machines (SVM).
  • Extracted and analyzed key EEG features such as Differential Entropy (DE), Power Spectral Density (PSD), and various Asymmetry and Causality measures.

Main Results:

  • The Graph Convolutional Neural Network (GCNN) achieved an average accuracy of 89.97% in subject-dependent emotion classification.
  • Deep Neural Network (DNN) reached 86.08% accuracy, outperforming SVM but with higher processing demands.
  • The study highlights the effectiveness of specific EEG features in distinguishing emotional states.

Conclusions:

  • Graph Convolutional Neural Networks (GCNN) demonstrate superior performance for EEG-based emotion detection.
  • Feature selection and model choice are critical for accurate and efficient emotion classification.
  • EEG-based emotion detection holds significant potential for real-world applications, emphasizing ethical considerations.