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科学通报  2015 

面向脑电数据的知识建模和情感识别

DOI: 10.1360/N972014-00829, PP. 1002-1009

Keywords: EEG,本体,情感识别,基于规则推理,随机森林

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Abstract:

心理科学研究依赖于对生理、心理数据的分析,情感是心理研究的重要内容.近年来随着认知神经科学研究技术的成熟,研究者利用脑电(electroencephalogram,EEG)等可以反映脑功能活动的生理信号,直接研究情感问题,如情感识别、情绪脑等.但是,生理信号将会产生TB级甚至PB级的数据量,认知研究和临床神经科学在过去几十年中已产生大量生理数据,对这些大数据的表示和情感知识挖掘需要更高级的工具.构建能够表示数据含义和情感相关知识的模型,能够给心理研究者提供一个知识共享平台,以便使用这些大数据进行情感方面的科学研究.本文构建一个可以表示EEG数据语义和被试者上下文信息的本体模型,并基于该模型使用推理引擎进行基于EEG生理信号数据的自动情感识别.实验结果表明,模型在eNTERFACE2006数据集上能够以99.11%的平均准确率识别被试者的情感状态,并从实验结果分析发现基于EEG数据情感识别最关键的特征是Beta波与Theta波的绝对功率比.

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