Visual attentiveness recognition using probabilistic neural network

Yi Chun Chen, Yi Jing Lin, I. Chieh Chen, Chia Ju Peng, Yu Jian Hu, Shih Jui Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationApplications of Machine Learning
EditorsMichael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. S. Awwal, Khan M. Iftekharuddin
ISBN (Electronic)9781510629714
StatePublished - 2019
EventApplications of Machine Learning 2019 - San Diego, United States
Duration: 13 Aug 201914 Aug 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceApplications of Machine Learning 2019
Country/TerritoryUnited States
CitySan Diego


  • Electroencephalogram
  • Machine learning
  • visual attentiveness


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