EEG and peripheral markers of viewer ratings: a study of short films - Kosonogov, Vladimir; Shelepenkov, Danila; Rudenkiy, Nikita (Frontiers in Neuroscience, 2023)
Cinema is an important part of modern culture, influencing millions of viewers. Research suggested many models for the prediction of film success, one of them being the use of neuroscientific tools. The aim of our study was to find physiological markers of viewer perception and correlate them to short film ratings given by our subjects. Short films are used as a test case for directors and screenwriters and can be created to raise funding for future projects; however, they have not been studied properly with physiological methods. We recorded electroencephalography (18 sensors), facial electromyography (corrugator supercilii and zygomaticus major), photoplethysmography, and skin conductance in 21 participants while watching and evaluating 8 short films (4 dramas and 4 comedies). Also, we used machine learning (CatBoost, SVR) to predict the exact rating of each film (from 1 to 10), based on all physiological indicators. In addition, we classified each film as low or high rated by our subjects (with Logistic Regression, KNN, decision tree, CatBoost, and SVC). The results showed that ratings did not differ between genres. Corrugator supercilii activity ("frowning" muscle) was larger when watching dramas; whereas zygomaticus major ("smiling" muscle) activity was larger during the watching of comedies. Of all somatic and vegetative markers, only zygomaticus major activity, PNN50, SD1/SD2 (heart rate variability parameters) positively correlated to the film ratings. The EEG engagement indices, beta/(alpha+theta) and beta/alpha correlated positively with the film ratings in the majority of sensors. Arousal (betaF3 + betaF4)/(alphaF3 + alphaF4), and valence (alphaF4/betaF4) - (alphaF3/betaF3) indices also correlated positively to film ratings. When we attempted to predict exact ratings, MAPE was 0.55. As for the binary classification, logistic regression yielded the best values (area under the ROC curve = .62) than other methods (.51-.60). Overall, we revealed EEG and peripheral markers, which reflect viewer ratings and can predict them to a certain extent. In general, high film ratings can reflect a fusion of high arousal and different valence, positive valence being more important. These findings broaden our knowledge about the physiological basis of viewer perception and can be potentially used at the stage of film production.