Why the study?
Emotion recognition using physiological signals is of interest in human-computer interaction, with EEG considered the most reliable modality for understanding emotion processing and perception.
Population
12 participants
Comparison
Happy vs sad emotion classification using time domain features from alpha and beta EEG frequency bands
Key result
Emotion classification using a Naïve Bayes classifier on combined alpha and beta EEG frequency bands achieved an accuracy of 87.5% for differentiating happy and sad emotions.
Authors
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Hypothesis-generating for EEG-based emotion detection; leaves open clinical translation pending larger validation studies.
A Naïve Bayes classifier using combined alpha and beta band time-domain features from EEG signals achieved 87.5% accuracy in classifying happy versus sad emotions.
Oktavia et al. (2019) studied Emotion recognition (n=12). Naïve Bayes learning classifier on EEG signals was evaluated on Accuracy of emotions recognition. Emotion classification using a Naïve Bayes classifier on combined alpha and beta EEG frequency bands achieved an accuracy of 87.5% for differentiating happy and sad emotions.