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.

中文翻译：

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EmotionMeter：识别人类情绪的多模式框架


在本文中，我们提出了一种名为 EmotionMeter 的多模式情感识别框架，它结合了脑电波和眼球运动。为了提高 EmotionMeter 在实际应用中的可行性和可佩戴性，我们设计了一个六电极放置在耳朵上方来收集脑电图 (EEG) 信号。我们结合脑电图和眼动来整合用户的内部认知状态和外部潜意识行为，以提高EmotionMeter的识别准确性。实验结果表明，与单一模态相比，多模态深度神经网络的模态融合可以显着提高性能，并且对于四种情绪（快乐、悲伤、恐惧和中性）实现了 85.11% 的最佳平均准确率。我们探讨了脑电图和眼动的表征能力的互补特征，并发现脑电图具有对快乐情绪进行分类的优势，而眼动在识别恐惧情绪方面优于脑电图。为了研究 EmotionMeter 随着时间的推移的稳定性，每个受试者在不同的日子进行三次实验。 EmotionMeter 在六电极脑电图和眼动特征的会话中获得了 72.39% 的平均识别准确率。这些实验结果证明了 EmotionMeter 在会话内和会话之间的有效性。