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Fear Detection Using Electroencephalogram and Artificial Intelligence: A Systematic Review.

Bladimir Serna1, Ricardo Salazar2, Gustavo A Alonso-Silverio3

  • 1Centro de Innovación, Competitividad y Sostenibilidad, Universidad Autónoma de Guerrero, Acapulco 39640, Guerrero, Mexico.

Brain Sciences
|August 28, 2025
PubMed
Summary

Artificial intelligence (AI) effectively detects fear using electroencephalogram (EEG) signals. Non-linear models combined with immersive stimuli achieved up to 92% accuracy, showing great potential for various applications.

Keywords:
EEG signal processingPRISMAaffective computingartificial intelligence (AI)brainwave analysisbrain–computer interface (BCI)electroencephalography (EEG)emotion recognitionfear detectionmachine learning (ML)neural signalsneurotechnology

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Area of Science:

  • Neuroscience
  • Computer Science
  • Affective Computing

Background:

  • Fear detection using electroencephalogram (EEG) signals is crucial for applications in mental health, affective computing, and intelligent safety systems.
  • Artificial intelligence (AI) offers advanced capabilities for analyzing complex EEG data to identify fear states.

Purpose of the Study:

  • To systematically review and identify the most effective AI methods, algorithms, and configurations for fear detection from EEG signals.
  • To synthesize findings on experimental paradigms, EEG devices, brainwave bands, and electrode placements that optimize fear detection accuracy.

Main Methods:

  • A systematic literature search was performed following PRISMA 2020 guidelines, using keywords related to fear detection, AI, and machine learning.
  • Eleven relevant studies were selected based on predefined inclusion and exclusion criteria, focusing on EEG-based fear detection.

Main Results:

  • Non-linear AI models, particularly Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), demonstrated high classification accuracy (up to 92%), especially with immersive stimulation.
  • Beta and gamma brainwave frequencies were consistently linked to fear responses.
  • Specific EEG devices (Emotiv, Biosemi), frontotemporal electrode placement, and proprietary datasets contributed to improved model performance.

Conclusions:

  • AI-powered EEG-based fear detection shows significant potential and rapid development.
  • This technology has broad interdisciplinary applications in healthcare, intelligent safety systems, and affective computing.