脑电图
眼动
计算机科学
情绪识别
人工智能
传感器融合
特征(语言学)
模式识别(心理学)
特征提取
融合
追踪
眼球运动
计算机视觉
语音识别
心理学
语言学
哲学
精神科
操作系统
作者
Wei‐Long Zheng,Bochen Dong,Bao‐Liang Lu
标识
DOI:10.1109/embc.2014.6944757
摘要
This paper presents a new emotion recognition method which combines electroencephalograph (EEG) signals and pupillary response collected from eye tracker. We select 15 emotional film clips of 3 categories (positive, neutral and negative). The EEG signals and eye tracking data of five participants are recorded, simultaneously, while watching these videos. We extract emotion-relevant features from EEG signals and eye tracing data of 12 experiments and build a fusion model to improve the performance of emotion recognition. The best average accuracies based on EEG signals and eye tracking data are 71.77% and 58.90%, respectively. We also achieve average accuracies of 73.59% and 72.98% for feature level fusion strategy and decision level fusion strategy, respectively. These results show that both feature level fusion and decision level fusion combining EEG signals and eye tracking data can improve the performance of emotion recognition model.
科研通智能强力驱动
Strongly Powered by AbleSci AI