Abstract—Preliminary results to a new approach for neurocognitive training on academic engagement and monitoring of attention levels in children with learning difficulties is presented. Machine Learning (ML) techniques and a Brain-Computer Interface (BCI) are used to develop an interactive AI-based game for educational therapy to monitor the progress of children’s concentration levels during specific cognitive tasks. Our approach resorts to data acquisition of brainwaves of children using electroencephalography (EEG) to classify concentration levels through model calibration. The real-time brainwave patterns are inputs to our game interface to monitor concentration levels. When the concentration drops, the educational game can personalize to the user by changing the challenge of the training or providing some new visual or auditory stimuli to the user in order to reduce the attention loss. To understand concentration level patterns, we collected brainwave data from children at various primary schools in Brazil who have intellectual disabilities e.g. autism spectrum disorder and attention deficit hyperactivity disorder. Preliminary results show that we successfully benchmarked (96%) the brainwave patterns acquired by using various classical ML techniques. The result obtained through the automatic classification of brainwaves will be fundamental to further develop our full approach. Positive feedback from questionnaires was obtained for both, the AI-based game and the engagement and motivation during the training sessions.
Index Terms—Technology for educational therapy, children with attention deficit, BCI, AI-based games, machine learning.
Diego R Faria and Jordan J. Bird are with ARVIS Lab, Computer Science Department, School of Engineering and Applied Science, Aston University, UK (e-mail: {d.faria. birdj1}@aston.ac.uk). Cintia Daquana is with Education Secretariat of Cambe-PR Brazil, Department of Special Education and Inclusion, Brazil (e-mail: educacaoinclusiva@cambe.pr.gov). Jhonatan Kobylarz is with Federal University of Parana, Brazil. Pedro Ayrosa is with LABTED-UEL and Computer Science, Londrina State University, Brazil (e-mail: ayrosa@uel.br).
Cite:Diego R. Faria, Jordan J. Bird, Cintia Daquana, Jhonatan Kobylarz, and Pedro P. S. Ayrosa, "Towards AI-based Interactive Game Intervention to Monitor Concentration Levels in Children with Attention Deficit," International Journal of Information and Education Technology vol. 10, no. 9, pp. 641-648, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).