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Neurofeedback and AI for Analyzing Child Temperament and Attention Levels

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11669)

Abstract

One of the common problems among preschool children is attention ability development. It is important to detect and identify earlier the attention problems which may minimize the harmful impact of childhood disorders. The purpose of this research is to predict and analyze the attention levels of children aged 4–7. Using parental report or subjective report to analyze the children’s psychological dimensions of temperament is a common approach for temperament research, but it may be bias. Electroencephalography (EEG) is a method to illustrate the brain electrical activity. We proposed a Neurofeedback Technology (NFT) system to amalgamate the collection of EEG signals data and Behavior Style Questionnaire (BSQ) for child temperament data by applying k-means algorithm, an Artificial Intelligence (AI) unsupervised machine learning, clustering analysis method, to observe children’s attention levels. The experimental results not only infer that the value of temperament with EEG classification could be consistent, but also provide a valid way to classify attention levels in specific time period. The combination of the parental subjective report with EEG data demonstrates a novel and valuable approach for resolving child attention problems. The results facilitate earlier identification of attention problems and support better parent-child understanding and interactions.

Keywords

  • Neurofeedback
  • Artificial Intelligence
  • EEG
  • Big data analysis
  • Attention
  • Temperament

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Acknowledgement

This research is supported by the Ministry of Education, Taiwan and Shih Chien University under grant USC-107-03-04010, USC-107-05-04006 and USC-108-08-04005.

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Correspondence to Anna Yu-Ju Yen .

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Lee, M.R., Yen, A.YJ., Chang, L. (2019). Neurofeedback and AI for Analyzing Child Temperament and Attention Levels. In: Ohara, K., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2019. Lecture Notes in Computer Science(), vol 11669. Springer, Cham. https://doi.org/10.1007/978-3-030-30639-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-30639-7_3

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