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Proceedings Paper

Visual attentiveness recognition using probabilistic neural network
Author(s): Yi-Chun Chen; Yi-Jing Lin; I-Chieh Chen; Chia-Ju Peng; Yu-Jian Hu; Shih-Jui Chen
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Paper Abstract

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

Paper Details

Date Published: 6 September 2019
PDF: 6 pages
Proc. SPIE 11139, Applications of Machine Learning, 1113915 (6 September 2019); doi: 10.1117/12.2527982
Show Author Affiliations
Yi-Chun Chen, National Central Univ. (Taiwan)
Yi-Jing Lin, National Central Univ. (Taiwan)
I-Chieh Chen, National Central Univ. (Taiwan)
Chia-Ju Peng, National Central Univ. (Taiwan)
Yu-Jian Hu, National Central Univ. (Taiwan)
Shih-Jui Chen, National Central Univ. (Taiwan)

Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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