DEAP: A Database for Emotion Analysis Using Physiological Signals
DEAP: A Database for Emotion Analysis Using Physiological Signals
复制标题
DOI：
10.1109/t-affc.2011.15
复制
发表时间：
2012-01-01
影响因子：
11.2
通讯作者：
Patras, Ioannis (Yiannis)
中科院分区：
文献类型：
--
作者：
Koelstra, Sander;Muhl, Christian;Patras, Ioannis (Yiannis)
我们提出了一个多模态数据集的分析人类情感状态。32名参与者在观看40段一分钟长的音乐录像片段时记录了脑电图（EEG）和外周生理信号。参与者根据唤醒程度、效价、喜欢/不喜欢、支配和熟悉程度对每个视频进行评级。对于32名参与者中的22名，还记录了正面视频。提出了一种新的刺激选择方法，使用检索情感标签从last.fm网站，视频亮点检测，和在线评估工具。在实验过程中的参与者的评级进行了广泛的分析。EEG信号频率和参与者的评级之间的相关性进行了研究。方法和结果的唤醒，效价和喜欢/不喜欢的评级使用的方式，脑电图，外周生理信号，多媒体内容分析的单次试验分类。最后，对不同模态的分类结果进行决策融合。该数据集是公开的，我们鼓励其他研究人员使用它来测试自己的情感状态估计方法。
We present a multimodal data set for the analysis of human affective states. The electroencephalogram (EEG) and peripheral physiological signals of 32 participants were recorded as each watched 40 one-minute long excerpts of music videos. Participants rated each video in terms of the levels of arousal, valence, like/dislike, dominance, and familiarity. For 22 of the 32 participants, frontal face video was also recorded. A novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool. An extensive analysis of the participants' ratings during the experiment is presented. Correlates between the EEG signal frequencies and the participants' ratings are investigated. Methods and results are presented for single-trial classification of arousal, valence, and like/ dislike ratings using the modalities of EEG, peripheral physiological signals, and multimedia content analysis. Finally, decision fusion of the classification results from different modalities is performed. The data set is made publicly available and we encourage other researchers to use it for testing their own affective state estimation methods.