For instant recognition of visual attentiveness, we established a set of studies based on signal conversion and machinelearning of electroencephalogram (EEG). In this work, we invited twelve participants who were asked to play testing gamesfor ensuing paying visual attention or to take a rest for a relaxed state. The brainwaves of participants were recorded by anEEG monitor during the experiments. EEG signals were transferred from time-domain into frequency-domain signals byfast Fourier transform (FFT) to obtain the frequency distributions of brainwaves of different visual attention states. Thefrequency information was then inputted into a probabilistic neural network (PNN) to build a discrimination model and tolearn the rules that could determine an EEG epoch belongs to paying attention or not. As a type of supervised feedforwardneural networks, PNN benefits high training speed and good error tolerance which is suitable for instant classificationtasks. Given a set of training samples, PNN can train the predictable model of the specific EEG features by supervisedlearning algorithm, performing a classifier for visual attentiveness. In this paper, the proposed method successfully offersefficient differentiation for the assessment of visual attentiveness using FFT and PNN. The predictive model candistinguish the EEG epoch with attentive or relaxed states, which has an average accuracy higher than 82% for twelveparticipants. This attention classifier is expected to aid smart lighting control, specifically in assessing how differentlighting situations will influence users’ visual work concentration.
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