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EEG emotion recognition using reduced channel wavelet entropy and average wavelet coefficient features with normal

Henry Candra, Mitchell Yuwono, Rifai Chai

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary

    This study introduces a new EEG feature, wavelet entropy and average wavelet coefficient (WEAVE), for emotion recognition. WEAVE simplifies analysis and reduces channels, achieving over 74% accuracy for valence and arousal emotions.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Affective Computing

    Background:

    • Emotion recognition from electroencephalogram (EEG) signals is challenging.
    • Complex features and numerous EEG channels are typically required.
    • Simpler analysis methods and reduced channel usage offer significant advantages.

    Purpose of the Study:

    • To explore a novel EEG-emotion feature, wavelet entropy and average wavelet coefficient (WEAVE).
    • To assess WEAVE's capability in classifying valence and arousal emotions.
    • To investigate feature complexity reduction using Normalized Mutual Information (NMI) for fewer EEG channels.

    Main Methods:

    • Developed a combined feature: wavelet entropy and average wavelet coefficient (WEAVE).
    • Employed Normalized Mutual Information (NMI) for dimensionality reduction and channel selection.
    • Classified valence and arousal emotions using the WEAVE feature and reduced channel sets.

    Main Results:

    • WEAVE feature achieved 76.8% accuracy for valence and 74.3% for arousal emotion classification.
    • NMI analysis indicated linear characteristics of the WEAVE feature, enabling channel reduction.
    • Identified distinct EEG channel combinations for valence versus arousal emotion detection with reduced channels.

    Conclusions:

    • The WEAVE feature is a promising, simplified approach for EEG-based emotion recognition.
    • NMI effectively reduces EEG channel requirements for WEAVE feature analysis.
    • Channel selection for valence and arousal emotion detection differs, suggesting distinct neural correlates.