September 21, 2025IbrainOpen Access

Emotional recognition while watching emotional videos: Based on electroencephalography signal analysis and machine learning models

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This research identifies five emotions using EEG signal analysis, suggesting machine learning can enhance emotional technology.

The study identifies five emotions—relaxation, happiness, motivation, sadness, and fear—with high classification accuracy.
EEG data from 23 male master's students revealed ensemble models achieved a peak accuracy of 95.38%.
Analysis involved preprocessing EEG signals, feature extraction, and classification using different machine learning techniques.
These findings may inform technology development aimed at better recognizing human emotions for improved interfaces.

Asl et al. (2025) studied this question.

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September 21, 2025IbrainOpen Access

View Full Paper

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Authors

This research identifies five emotions using EEG signal analysis, suggesting machine learning can enhance emotional technology.

The study identifies five emotions—relaxation, happiness, motivation, sadness, and fear—with high classification accuracy.
EEG data from 23 male master's students revealed ensemble models achieved a peak accuracy of 95.38%.
Analysis involved preprocessing EEG signals, feature extraction, and classification using different machine learning techniques.
These findings may inform technology development aimed at better recognizing human emotions for improved interfaces.

Asl et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark