Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasas. Rasa—as opposed to a pure emotion—is defined as a superposition of certain transitory, dominant, and temperamental emotional states. Although Rasas have been widely discussed in the text, dedicated brain imaging studies have not been conducted in their research. Our study examines the neural oscillations, recorded through electroencephalography (EEG) imaging, that are elicited while experiencing emotional states corresponding to Rasas. We identify differences among them using network-based functional connectivity metrics in five different frequency bands. Further, Random Forest models are trained on the extracted network features, and we present our findings based on classifier predictions. We observe slow (delta) and fast brain waves (beta and gamma) exhibited the maximum discriminating features between Rasas, whereas alpha and theta bands showed fewer distinguishable pairs. Out of nine Rasas, Sringaram (love), Bibhatsam (odious), and Bhayanakam (terror) were distinguishable from other Rasas the most across frequency bands. On the scale of most network metrics, Raudram (rage) and Sringaram are on the extremes, which also resulted in their good classification accuracy of 95%. This is reminiscent of the circumplex model where anger and contentment/happiness are on extremes on the pleasant scale. Interestingly, our results are consistent with the previous studies which highlight the significant role of higher frequency oscillations in the classification of emotions, in contrast to the alpha band that has shows non-significant differences across emotions. This research contributes to one of the first attempts to investigate the neural correlates of Rasas. Therefore, the results of this study can potentially guide the explorations into the entrainment of brain oscillations between performers and viewers, which can further lead to better performances and viewer experience.

中文翻译：

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使用网络指标对与印度 Rasas 相关的大脑振荡活动进行分类

西方情绪分类的神经特征已在文献中得到广泛讨论。古印度关于表演艺术的论文 Natyashastra 将情绪分为九类，称为 Rasas。与纯粹的情绪相反，味被定义为某些短暂的、主导的和喜怒无常的情绪状态的叠加。尽管文中对 Rasas 进行了广泛讨论，但他们的研究中并未进行专门的脑成像研究。我们的研究检查了通过脑电图 (EEG) 成像记录的神经振荡，这些振荡是在体验与 Rasas 对应的情绪状态时引发的。我们使用五个不同频段中基于网络的功能连接指标来识别它们之间的差异。进一步，随机森林模型在提取的网络特征上进行训练，我们根据分类器预测展示我们的发现。我们观察到慢脑电波（δ）和快脑电波（β 和伽马）在 Rasas 之间表现出最大的区分特征，而 alpha 和 theta 波段显示出较少的可区分对。在九种 Rasas 中，Sringaram（爱）、Bibhatsam（可恶）和 Bhayanakam（恐怖）在不同频段上与其他 Rasas 的区别最大。在大多数网络指标的规模上，Raudram（愤怒）和 Sringaram 处于极端，这也导致它们的分类准确率达到 95%。这让人想起环状模型，其中愤怒和满足/幸福在愉快的尺度上处于极端。有趣的是，我们的结果与之前的研究一致，这些研究强调了高频振荡在情绪分类中的重要作用，而 alpha 波段在不同情绪中显示出不显着的差异。这项研究有助于研究 Rasas 的神经相关性的首次尝试之一。因此，这项研究的结果可能会指导对表演者和观众之间大脑振荡夹带的探索，从而进一步带来更好的表演和观众体验。