A Framework for Human Learning Ability Study Using Simultaneous EEG/fNIRS and Portable EEG for Learning and Teaching Development

Abstract

In ageing society, it is not only that we need better care and treatment to maintain the quality of life of elderly population but also need better ways to strengthen the development of our children so that we could live in a healthy ageing society. This study proposed a framework for human learning ability study by using multimodal neuroimaging through simultaneous EEG/fNIRS measurement and neuroinformatics to understand target learning ability in laboratory and using portable EEG device to monitor real-time brain state for evaluating the learning/teaching methods introduced for improving the target learning ability in learning environment. By incorporating neuroscience approach in both laboratory and learning environment, not only scientific findings from neuroscience studies could give implications to educators to improve students' learning, but also the developed learning/teaching methods could be assessed their effectiveness in classroom practice. This framework may help contribute in bridging the gap between neuroscience and education, which is the aim of educational neuroscience. Toward smart education, the proposed conceptual framework deploys truly brain-based learning/teaching approach to enhance students' learning to better acquire the knowledge and cognitive skills.

Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research

“…To overcome these challenges, recent efforts have been made to combine EEG and fNIRS into bimodal systems able the simultaneously record the hemodynamic and electrophysiological correlates of human performance in real time (Safaie et al, 2013 ; Tomita et al, 2014 ; Hong and Khan, 2017 ; von Luhmann and Muller, 2017 ; Hong et al, 2018 ). Indeed, besides improving the signal-to-noise ratio by representing brain activity in different formats (Sun et al, 2020 ), combined EEG and fNIRS signals have the potential to significantly compensate for each other's spatial and temporal limitations, thereby increasing the speed, precision, and richness of mental state decoding in various tasks of interest to human factors (Wallois et al, 2010 , 2012 ; Nguyen et al, 2012 , 2017 ; Tomita et al, 2014 ; Kaewkamnerdpong, 2016 ; Balconi et al, 2017 ; Pinti et al, 2018b ; Dehais et al, 2019 ; Firooz and Setarhdan, 2019 ). They may ultimately provide motor-disabled individuals with the means to interact with their environment and optimize the detection of mental workload, drowsiness, or brain dysfunction in various high-risk situations.…”