Discrimination between different emotional states based on the chaotic behavior of galvanic skin responses
Discrimination between different emotional states based on the chaotic behavior of galvanic skin responses
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DOI：
10.1007/s11760-017-1092-9
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发表时间：
2017-10-01
影响因子：
2.3
通讯作者：
Daneshvar, Sabalan
中科院分区：
文献类型：
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作者：
Goshvarpour, Atefeh;Abbasi, Ataollah;Daneshvar, Sabalan
本研究的目的是探讨皮肤电反应（GSR）的有效性，情绪识别使用非线性方法。对35名健康学生在听情绪化音乐片段时的GSR进行了记录。信号的非线性特征得到了全面表征。应用三种降维方法，包括顺序前向选择（SFS），顺序浮动前向选择，和随机子集特征选择（RSFS）结合四种分类方法，包括K-最近邻，最小二乘支持向量机，Fisher判别分析，二次分析，情感类之间的区分进行了评估。此外，还检查了两种分类策略，包括二进制（BIC）和一个与休息。结果表明，Fisher识别率较高。在这种情况下，BIC在所有情绪状态和所有特征选择方法中的准确率都高于99%。RSFS和Fisher在悲伤中的最高分类率为99.98%。在所有情绪类别中，和平和恐惧的识别率更高。这项研究表明，非线性GSR特征可以提供一个信息的措施，调查在不同的情绪状态的生理波动在音乐。
The purpose of the current study was to examine the effectiveness of galvanic skin responses (GSRs) in emotion recognition using nonlinear approaches. GSR of 35 healthy students was recorded while subjects were listening to emotional music clips. The signals were comprehensively characterized by nonlinear features. Applying three dimensionality reduction methods, including sequential forward selection (SFS), sequential floating forward selection, and random subset feature selection (RSFS) in combination with four classification approaches, including K-nearest neighbor, least-square support vector machine, Fisher discriminant analysis, and quadratic analysis, discrimination between emotional classes was evaluated. In addition, two classification strategies were examined, including binary (BIC) and one vs. rest. The results showed that higher recognition rates were achieved for Fisher. In this case, the BIC accuracy rates were higher than 99% in all emotional states and all feature selection methodologies. The maximum classification rate of 99.98% was obtained using RSFS and Fisher in sadness. Among all emotion categories, better recognition rates were achieved for peacefulness and fear. This study demonstrates that nonlinear GSR characteristics can provide an informative measure to investigate the physiological fluctuations in different emotional states during music.