【摘要】 情绪是人对外界事物产生的心理和生理反应.准确地识别情绪在人机交互研究中占据着重要位置,其成果可应用在医学、教育、心理、军事等方向.由于脑电信号具有客观,不易伪装等特点,其在情绪识别领域的应用广受关注.从脑电信号中提取与情绪关联大、区分能力强的特征,有助于后续的分类器更有效地识别不同情绪状态.本文调研了目前常用于情绪识别研究领域的脑电信号特征,从时域、频域、时频域和空间域4个方面介绍其定义、计算方法,以及与情绪的联系,在SEED, DREAMER和CAS-THU 3个公开的脑电–情绪数据集上,使用SLDA算法评估和比较了各类脑电特征区分不同效价的能力.本文也对目前存在的问题和未来的研究方向进行了讨论和展望,可以为研究人员系统性地了解面向情绪识别的脑电特征研究现状以及开展后续研究提供思路.更多还原

【Abstract】 Emotion recognition is an important research topic in the human-machine interaction field, and it can be applied to medicine, education, psychology, military, and other areas. Electroencephalogram(EEG) signals are mostly used among various indices of emotion recognition. High accuracy of emotion classifiers can be achieved by extracting the most relevant and discriminant features of emotion states. This study surveys EEG features that are extensively used in current emotion recognition studies by introducing EEG features from the following four viewpoints: time domain, frequency domain, time–frequency domain, and space domain. An SLDA algorithm is imported to three public EEG-emotion datasets(SEED, DREAMER, and CAS-THU) to evaluate feature capabilities that distinguish emotion valence. Existing problems and future investigations are also discussed in this paper.更多还原