Accurately predicting whether a short video will be liked by viewers is a topic of interest to media researchers. This study used an electroencephalogram (EEG) to record neural activity in 109 participants as they watched short videos (16 clips per person) to see which neural signals reflected viewers' preferences. The results showed that, compared with the short videos they disliked, individuals would experience positive emotions [indexed by a higher theta power, lower (beta - theta)/(beta + theta) score], more relaxed states (indexed by a lower beta power), lower levels of mental engagement and alertness [indexed by a lower beta/(alpha + theta) score], and devote more attention (indexed by lower alpha/theta) when watching short videos they liked. We further used artificial neural networks to classify the neural signals of different preferences induced by short videos. The classification accuracy was the highest when using data from bands over the whole brain, which was 75.78%. These results may indicate the potential of EEG measurement to evaluate the subjective preferences of individuals for short videos.
Keywords: EEG; machine learning; preferences; short videos.
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