EmotionMeter: A Multimodal Framework for Recognizing Human Emotions

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

    Результат исследований: Вклад в журналСтатьярецензирование

    266 Цитирования (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.

    Язык оригиналаАнглийский
    Номер статьи8283814
    Страницы (с-по)1110-1122
    Число страниц13
    ЖурналIEEE Transactions on Cybernetics
    Номер выпуска3
    СостояниеОпубликовано - мар. 2019


    Подробные сведения о темах исследования «EmotionMeter: A Multimodal Framework for Recognizing Human Emotions». Вместе они формируют уникальный семантический отпечаток (fingerprint).