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Affective Recognition in Dynamic and Interactive Virtual Environments
IEEE Transactions on Affective Computing ( IF 11.2 ) Pub Date : 2020-01-01 , DOI: 10.1109/taffc.2017.2764896
Mohammadhossein Moghimi , Robert Stone , Pia Rotshtein

The past decade has witnessed a significant increase in interest in human emotional behaviours in the future of interactive multimodal computing. Although much consideration has been given to non-interactive affective stimuli (e.g., images and videos), the recognition of emotions within interactive virtual environments has not received an equal level of attention. In the present study, a psychophysiological database, cataloguing the EEG, GSR and heart rate of 30 participants, exposed to an affective virtual environment, has been constructed. 743 features were extracted from the physiological signals. Then, by employing a feature selection technique, the dimensionality of the feature space was reduced to a smaller subset, containing only 30 features. Four classification techniques (KNN, SVM, Discriminant Analysis (DA) and Classification Tree) were employed to classify the affective psychophysiological database into four Affective Clusters (derived from a Valence-Arousal space) and eight Emotion Labels. By employing cross-validation techniques, the performances of more than a quarter of a million different classification settings (various window lengths, classifier settings, etc.) were investigated. The results suggested that the physiological signals could be employed to classify emotional experiences, with high precision. The KNN and SVM outperformed both Classification Tree and DA classifiers; with 97.01 percent and 92.84 percent mean accuracies, respectively.

中文翻译:

动态和交互式虚拟环境中的情感识别

在过去的十年中,在交互式多模态计算的未来,人们对人类情感行为的兴趣显着增加。尽管已经对非交互式情感刺激(例如图像和视频)给予了很多考虑,但交互式虚拟环境中的情绪识别并未受到同等程度的关注。在本研究中,已经构建了一个心理生理学数据库,对暴露于情感虚拟环境的 30 名参与者的 EEG、GSR 和心率进行编目。从生理信号中提取了 743 个特征。然后,通过使用特征选择技术,特征空间的维数减少到一个更小的子集,只包含 30 个特征。四种分类技术(KNN、SVM、判别分析(DA)和分类树)被用来将情感心理生理数据库分为四个情感集群(来自价-唤醒空间)和八个情感标签。通过采用交叉验证技术,研究了超过一百万种不同分类设置(各种窗口长度、分类器设置等)的性能。结果表明,生理信号可用于对情绪体验进行高精度分类。KNN 和 SVM 优于分类树和 DA 分类器;平均准确率分别为 97.01% 和 92.84%。通过采用交叉验证技术,研究了超过一百万种不同分类设置(各种窗口长度、分类器设置等)的性能。结果表明,生理信号可用于对情绪体验进行高精度分类。KNN 和 SVM 优于分类树和 DA 分类器;平均准确率分别为 97.01% 和 92.84%。通过采用交叉验证技术,研究了超过一百万种不同分类设置(各种窗口长度、分类器设置等)的性能。结果表明,生理信号可用于对情绪体验进行高精度分类。KNN 和 SVM 优于分类树和 DA 分类器;平均准确率分别为 97.01% 和 92.84%。
更新日期:2020-01-01
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