August 4, 2025Brain SciencesOpen Access

Fear Detection Using Electroencephalogram and Artificial Intelligence: A Systematic Review

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Fear detection through EEG signals shows significant growth and potential in various applications.
Up to 92% classification accuracy was achieved using non-linear models with immersive stimulation.
Key EEG devices and experimental paradigms, such as videos and games, were examined for their effectiveness.
Beta and gamma brainwave frequencies are consistently linked to fear states, improving detection models.

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This systematic review identifies effective methods and algorithms for fear detection using electroencephalogram and artificial intelligence.

Serna et al. (2025) studied this question.

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This systematic review identifies effective methods and algorithms for fear detection using electroencephalogram and artificial intelligence.