EmotionMeter: A Multimodal Framework for Recognizing Human Emotions

Wei Long Zheng, Wei Liu, Yifei Lu, Bao Liang Lu, Andrzej Cichocki

    Research output: Contribution to journalArticlepeer-review

    242 Citations (Scopus)


    In this paper, we present a multimodal emotion recognition framework called EmotionMeter that combines brain waves and eye movements. To increase the feasibility and wearability of EmotionMeter in real-world applications, we design a six-electrode placement above the ears to collect electroencephalography (EEG) signals. We combine EEG and eye movements for integrating the internal cognitive states and external subconscious behaviors of users to improve the recognition accuracy of EmotionMeter. The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 85.11% is achieved for four emotions (happy, sad, fear, and neutral). We explore the complementary characteristics of EEG and eye movements for their representational capacities and identify that EEG has the advantage of classifying happy emotion, whereas eye movements outperform EEG in recognizing fear emotion. To investigate the stability of EmotionMeter over time, each subject performs the experiments three times on different days. EmotionMeter obtains a mean recognition accuracy of 72.39% across sessions with the six-electrode EEG and eye movement features. These experimental results demonstrate the effectiveness of EmotionMeter within and between sessions.

    Original languageEnglish
    Article number8283814
    Pages (from-to)1110-1122
    Number of pages13
    JournalIEEE Transactions on Cybernetics
    Issue number3
    Publication statusPublished - Mar 2019


    • Affective brain-computer interactions
    • deep learning
    • EEG
    • emotion recognition
    • eye movements
    • multimodal deep neural networks


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