Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan.
Department of Computer Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan.
Comput Biol Med. 2019 Nov;114:103469. doi: 10.1016/j.compbiomed.2019.103469. Epub 2019 Sep 27.
Human emotions are recognized in response to content engaging one (audio music) or two human senses (videos). An enhanced sensation with a more realistic feel could be achievable by engaging more than two human senses. In this study, olfaction enhanced multimedia content is generated by synchronizing traditional multimedia content with an olfaction dispenser for engaging olfactory sense in addition to vision and auditory senses. Brain activity of 20 participants (10 males and 10 females) is recorded with a commercially available EEG headband, while engaging with traditional and olfaction enhanced multimedia content. The human brain activity is used to analyze and differentiate the content engaging two (traditional multimedia content) or more than two (olfaction enhanced multimedia content) human senses. For brain activity analysis, we apply a t-test on the power spectra of five frequency sub-bands (delta, theta, alpha, beta, and gamma) of the acquired EEG data in response to traditional and olfaction enhanced multimedia. We observe that alpha, theta, and delta bands are significant in discriminating the response to traditional and olfaction enhanced multimedia content. High brain activity is observed in alpha, theta, and delta bands of frontal channels, while experiencing the olfaction enhanced multimedia content. A user-independent pleasantness classification based on human brain activity is also presented, where classification performance is measured using 10-fold cross validation. We extract features in frequency domain i.e., rational asymmetry (RASM) and differential asymmetry (DASM) from five EEG bands to classify two pleasantness states based on their valence scores using support vector machine (SVM) classifier. Features are further selected based on EEG electrode pair positions and sub-bands. We observed that RASM and DASM features selected from delta band (olfaction enhanced content), and alpha or gamma bands (traditional multimedia content) gives best classification accuracy. We achieved an accuracy of 75%, sensitivity of 77.7%, and specificity of 72.7% in response to olfaction enhanced multimedia content and an accuracy of 68.7%, sensitivity of 71.4%, and specificity of 69.2% in response to traditional multimedia content in classifying pleasant and unpleasant states using SVM. We observed that classification of pleasant state was comparatively better with olfaction enhanced multimedia content than traditional multimedia content.
人类情感是通过感知内容来识别的，这些内容可以通过单一感官（如音频音乐）或两种感官（如视频）来激发。如果能够同时调动多种感官，那么人类就可以获得更加强烈和逼真的体验。在这项研究中，我们通过同步传统多媒体内容和气味分配器来生成气味增强型多媒体内容，从而调动嗅觉以外的视觉和听觉感官。我们使用商用 EEG 头带记录了 20 名参与者（10 名男性和 10 名女性）的大脑活动，让他们在参与传统多媒体内容和气味增强型多媒体内容时记录大脑活动。我们使用人类大脑活动来分析和区分两种感官（传统多媒体内容）或多种感官（气味增强型多媒体内容）参与的内容。对于大脑活动分析，我们在响应传统和气味增强多媒体内容时，对获取的 EEG 数据的五个频带（δ、θ、α、β 和γ）的功率谱进行 t 检验。我们观察到，α、θ 和δ 频段在区分传统多媒体内容和气味增强多媒体内容的反应方面具有重要意义。在体验气味增强型多媒体内容时，额通道的α、θ 和δ 频段的大脑活动较高。我们还提出了一种基于人类大脑活动的用户独立愉悦度分类方法，使用 10 倍交叉验证来衡量分类性能。我们从五个 EEG 频段提取频域特征，即有理不对称（RASM）和差分不对称（DASM），然后使用支持向量机（SVM）分类器根据效价评分对两种愉悦状态进行分类。我们进一步根据 EEG 电极对位置和子频段选择特征。我们观察到，从 delta 频段（气味增强内容）、alpha 或 gamma 频段（传统多媒体内容）选择的 RASM 和 DASM 特征在识别气味增强多媒体内容时具有最佳分类准确性。在识别气味增强多媒体内容的愉悦状态时，SVM 的准确率为 75%，灵敏度为 77.7%，特异性为 72.7%；在识别传统多媒体内容的愉悦状态时，SVM 的准确率为 68.7%，灵敏度为 71.4%，特异性为 69.2%。我们观察到，与传统多媒体内容相比，气味增强型多媒体内容对愉悦状态的分类效果更好。
