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计算机系统应用:2020,29(5):233-238
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基于便携式脑电设备的快乐和悲伤情绪分类
(1.北京市地铁运营有限公司 地铁运营技术研发中心 地铁运营安全保障技术北京市重点实验室, 北京 100044;2.上海帝仪科技有限公司, 上海 200232)
Classification of Happiness and Sadness Based on Portable EEG Devices
(1.Beijing Key Laboratory of Subway Operation Safety Technology cum. Subway Operation Technology R&D Centre, Beijing Subway Operation Co. Ltd., Beijing 100044, China;2.Shanghai Deayea Technology Co. Ltd., Shanghai 200232, China)
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投稿时间:2019-09-04    修订日期:2019-10-08
中文摘要: 驾驶员情绪状态的识别对车辆主动安全技术的研究具有重要的应用价值.本研究通过情绪视频诱发的方法采集17位被试前额双通道脑电信号,提取不同情绪的脑电特征,并对数据进行降维处理后采用多种分类器进行情绪分类.结果显示,与单核分类器和集成学习分类器相比,基于梯度提升决策树(GBDT)算法得到快乐和悲伤的识别准确率最高.本研究为驾驶员情绪状态的实时监测和识别提供新方法,为提高行车的安全性提供了理论保障.
中文关键词: 脑电  情绪  快乐  悲伤  梯度提升决策树
Abstract:There is an important application value for the research of vehicle active safety technology through the recognition of drivers’ emotional state. In this study, seventeen subjects’ frontal dual-channel EEG signals were collected by emotional video induction method, and EEG characteristics of different emotions were extracted. After dimensionality reduction, the data were classified by multiple classifiers. The results show that compared with single-core classifier and ensemble learning classifier, Gradient Boosting Decision Tree (GBDT) algorithm has the highest recognition accuracy of happiness and sadness. This study provides a new method for real-time monitoring and recognition of drivers’ emotional state, and provides a theoretical guarantee for improving driving safety.
文章编号:7394     中图分类号:    文献标志码:
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姚娟娟,路堃,许金秀.基于便携式脑电设备的快乐和悲伤情绪分类.计算机系统应用,2020,29(5):233-238
YAO Juan-Juan,LU Kun,XU Jin-Xiu.Classification of Happiness and Sadness Based on Portable EEG Devices.COMPUTER SYSTEMS APPLICATIONS,2020,29(5):233-238

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