Scientists have developed a system that, they claim, can track eye movements to enable a computer to quickly and accurately identify images that a user finds positive or negative.
Based on eye-tracking and electro-encephalographic (EEG) readings, the breakthrough came from researchers Mingqing Yang, Li Lin, and Slavko Milekic from Oxford University.
Based on tracking what the user is looking at and measuring the electrical activity in their brain to determine whether they're pleased or not, the system knows what users like and dislike.
"To improve the quality of human-computer interaction, it is important for computers to be able to identify the images quickly that can trigger pleasant feelings and create a good user experience," the university researchers said.
"To reach this goal, the effective classification of images is an important prerequisite."
Prior research into effective image classification used image features as categorical measurable signals. However, these older techniques failed to provide a good correlation between the signal properties of image features and the expected effective experience of the viewer, leaving the research unable to bridge the effective gap."
The solution is this fresh system, which tracks the user's eye movements while measuring their brain using an EEG reader. By taking screenings of eye movement indices and EEG indices correlated with whether the user found something positive, negative, or neutral, the team built an "experience space" ready for analysis.
"Physiological experience data from the experience space were extracted, analysed mathematically and normalised to obtain parametric physiological experience data," the researchers' new paper claims.
"Using a multiple linear regression technique, we connected the participants' affective states and physiological experience data. We developed a quantitative mapping between the affective experience states and the sample images to acquire the classification of affective images."
The efficacy of the system was tested using abstract art. Participants were given 16 paintings, and the system categorised them into positive, negative, or neutral based on the physiological response data. The result was accurate classification of which images would lead to a sensation of pleasure in the majority of participants.
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