The HbO-based binary classification accuracies between positive emotion clusters are shown in Table 4.
The HbR-based results are shown in Table 5. The current study investigated the brain hemodynamic responses to different positive emotions using fNIRS. In line with our previous EEG study Hu et al. The three positive emotion clusters showed different hemodynamic responding patterns, and the HbO-based binary classification between the three clusters achieved an averaged accuracy of To the best of our knowledge, this is the first piece of fNIRS evidence demonstrating the differentiation of subdivided positive emotions.
In the present study, we found a mild correlation between the general emotion valence and lateral HbO activations, which suggests higher HbO responses in the lateral PFC were associated with more positive emotions.
Why do we have different feelings? - Charan and Aishwarya V., 10 & 8, Rutherford, New Jersey
Although we cannot make further inference due to different experimental paradigms, these findings nevertheless indicated the importance of PFC in positive emotion processing. This may be because that the conclusion in Nishitani et al. Another alternative explanation is that our stimuli contained not only maternal love but also romantic love, and further comparisons could be conducted between more specific kinds of love in future studies. It should be noted that these correlation results were non-significant and therefore mainly for a descriptive purpose.
The non-significance of these results might be due to the small sample size, or a possible high inter-participant variability of the positive emotion responses e.
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Further studies are necessary to localize the responsive regions of different positive emotions. More importantly, the hemodynamic signal based classification results confirmed the separability of these specific positive emotion clusters average accuracy for paired classifications between the three clusters achieved The HbO-based classifications in general showed better performance than HbR-based ones, which could be explained by the overall more reliable measurement of cerebral blood flow by HbO than HbR Malonek et al.
These binary classification accuracies were lower than results obtained for positive vs. Nevertheless, the discriminability between the three positive emotion clusters was still well above chance level. In addition, the classifications were performed on an individual level at the time scale of 10 s without any artifact rejection procedures.
While the neural separability between different positive emotion categories might be underestimated, these results offered direct support for the potential practical real-time emotion recognition applications. It is worthwhile to note that a machine learning approach was employed in the present study.
Here we mainly focused on single-participant-level classification results to reflect the separability of the neural responses to different positive emotions. While the sample size is smaller than typical neuroscience studies that have usually focused on group-level statistics, it is comparable with existing fNIRS based affective computing studies using machine learning methods e.
Moreover, our results for positive vs. Nevertheless, the machine learning approach is limited in its explanatory capacity toward mechanism interpretations Shmueli, , further studies with a larger sample size would help to gain more insights about underlying neural mechanisms of different positive emotions. Admittedly, several limitations of the present study should be noted. Accordingly, the three clusters based on these 10 positive emotions could not be expected to explain all the variants of positive emotions.
Second, as mentioned above, different emotion-eliciting paradigms might lead to different conclusions.