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Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for the correlation coefficients between the subjective ratings on the ten positive emotions per film clip and the corresponding EEG spectral powers in different frequency bands. Based on the similarities of the participants’ ratings on the ten positive emotions, these emotions were further clustered into three representative clusters, as ‘encouragement’ for awe, gratitude, hope, inspiration, pride, ‘playfulness’ for amusement, joy, interest, and ‘harmony’ for love, serenity. Using the EEG spectral powers as features, both the binary classification on the higher and lower ratings on these positive emotions and the binary classification between the three positive emotion clusters, achieved accuracies of approximately 80% and above. To our knowledge, our study provides the first piece of evidence on the EEG correlates of different positive emotions.

The behavioral differentiation of positive emotions has recently been studied in terms of their discrete adaptive functions or appraising profiles. Some preliminary neurophysiological evidences have been found with electroencephalography or autonomic nervous system measurements such as heart rate, skin conductance, etc. However, the brain’s hemodynamic responses to different positive emotions remain largely unknown. In the present study, the functional near-infrared spectroscopy (fNIRS) technique was employed. With this tool, we for the first time reported recognizable discrete positive emotions using fNIRS signals. Thirteen participants watched 30 emotional video clips to elicit 10 typical kinds of positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love), and their frontal neural activities were simultaneously recorded with a 24-channel fNIRS system. The multidimensional scaling analysis of participants’ subjective ratings on these 10 positive emotions revealed three distinct clusters, which could be interpreted as “playfulness” for amusement, joy, interest, “encouragement” for awe, gratitude, hope, inspiration, pride, and “harmony” for love, serenity. Hemodynamic responses to these three positive emotion clusters showed distinct patterns, and HbO-based individual-level binary classifications between them achieved an averaged accuracy of 73.79 ± 11.49% (77.56 ± 7.39% for encouragement vs. harmony, 73.29 ± 11.87% for playfulness vs. harmony, 70.51 ± 13.96% for encouragement vs. harmony). Benefited from fNIRS’s high portability, low running cost and the relative robustness against motion and electrical artifacts, our findings provided support for implementing a more fine-grained emotion recognition system with subdivided positive emotion categories.

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The emerging field of affective computing focuses on enhancing computers’ ability to understand and appropriately respond to people’s affective states in human-computer interactions, and has revealed significant potential for a wide spectrum of applications. Recently, the electroencephalography (EEG) based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application. The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology, psychology, and neuroscience. On the theoretical side, we suggest that researchers should be well aware of the limitations of the commonly used emotion models, and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms. On the practical side, we propose several operational recommendations for the challenges about data collection, feature extraction, model implementation, online system design, as well as the potential ethical issues. The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.

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BACKGROUND Photosynthetic pigments perform critical physiological functions in tea plants. Their content is an essential indicator of photosynthetic efficiency and nutritional status. The present study aimed to predict chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (total Chl), and carotenoid (Car) content in tea leaves under different levels of nitrogen treatment using hyperspectral imaging (HSI) in combination with variable selection algorithms. RESULTS A total of 150 samples were collected and scanned using the HSI system. The mean spectrum in the region of interest (ROI) was extracted, and the pigment content was measured by traditional chemical methods. Five and seven optimal wavelengths (OWs) were selected using the regression coefficients (RCs) of partial least squares regression (PLSR) and the second‐derivative (2‐Der), respectively. The optimal 2‐Der‐PLSR models for Chl a, Chl b, total Chl, and Car performed remarkably well based on seven OWs with correlation coefficients of prediction (RP) of 0.9337, 0.9322, 0.9333 and 0.9036, root mean square errors in prediction (RMSEP) of 0.1100, 0.0511, 0.1620, and 0.0300 mg g‐1, respectively. CONCLUSION The results of this study revealed that HSI combined with variable selection method can be employed as a rapid and accurate method for predicting the content of pigments in tea plants. © 2018 Society of Chemical Industry

During the jointing stage, the frost resistance of young ears (FRYE) was tested each day for the main stem, and also for the first, second and third tillers of the wheat cultivars Jinmai 47 and Jing 411. At the same time, the developmental progression of young ears (DPYE) of the same four shoots was also recorded each day. In the shoots of both cultivars, FRYE decreased as development advanced through the jointing stage. FRYE dropped off particularly sharply at the point when the anther connective tissue formation phase (ACFP) started. Shoots developing later, though with lower levels of soluble sugar, tended to suffer less from frost injury than those developing earlier. Frost resistance of 12 cultivars (six early‐ and six late‐maturing) was evaluated at ACFP. The results indicate that only one cultivar (Xin 11) is frost resistant, with no significant differences appearing among the other 11 cultivars. The results suggest that DPYE is an important factor affecting FRYE. Evaluation of frost resistance of wheat cultivars should thus be performed at the same phase to obtain a true measure of frost resistance. The early ACFP phase is suggested as being the most appropriate one for frost resistance evaluation.