Owing to the large number of video programs available, a method for accessing preferred videos efficiently through personalized video summaries and clips is needed. The automatic recognition of user states when viewing a video is essential for extracting meaningful video segments. Although there have been many studies on emotion recognition using various user responses, electroencephalogram (EEG)‐based research on preference recognition of videos is at its very early stages. This paper proposes classification models based on linear and nonlinear classifiers using EEG features of band power (BP) values and asymmetry scores for four preference classes. As a result, the quadratic‐discriminant‐analysisbased model using BP features achieves a classification accuracy of 97.39% (±0.73%), and the models based on the other nonlinear classifiers using the BP features achieve an accuracy of over 96%, which is superior to that of previous work only for binary preference classification. The result proves that the proposed approach is sufficient for employment in personalized video segmentation with high accuracy and classification power.

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Commercialized electroencephalography (EEG) sensors are available that one could extract EEG data more cheaply and more easily. As commercialized EEG sensors can be used commonly, the services that could be provided using EEG and interactions that can be achieved by EEG are needed to be studied. In this paper, we show the feasibility of integrating EEG based services and interactions into consumer electronics using commercialized EEG sensors. We use support vector machine (SVM) classifiers to classify the user's status using EEG data gathered from objects of interest and noise. The results show that EEG gathered from commercialized EEG sensors can be used to classify the user's status.

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