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|Title:||Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network||Authors:||Chattopadhyay, Soham
Prasad, Dilip K.
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2021||Source:||Chattopadhyay, S., Zary, L., Quek, C. & Prasad, D. K. (2021). Motivation detection using EEG signal analysis by residual-in-residual convolutional neural network. Expert Systems With Applications, 184, 115548-. https://dx.doi.org/10.1016/j.eswa.2021.115548||Journal:||Expert Systems with Applications||Abstract:||While we know that motivated students learn better than non-motivated students but detecting motivation is challenging. Here we present a game-based motivation detection approach from the EEG signals. We take an original approach of using EEG-based brain computer interface to assess if motivation state is manifest in physiological EEG signals as well, and what are suitable conditions in order to achieve the goal? To the best of our knowledge, detection of motivation level from brain signals is proposed for the first time in this paper. In order to resolve the central obstacle of small EEG datasets containing deep features, we propose a novel and unique ‘residual-in-residual architecture of convolutional neural network (RRCNN)’ that is capable of reducing the problem of over-fitting on small datasets and vanishing gradient. Having accomplished this, several aspects of using EEG signals for motivation detection are considered, including channel selection and accuracy obtained using alpha or beta waves of EEG signals. We also include a detailed validation of the different aspects of our methodology, including detailed comparison with other works as relevant. Our approach achieves 89% accuracy in using EEG signals to detect motivation state while learning, where alpha wave signals of frontal asymmetry channels are employed. A more robust (less sensitive to learning conditions) 88% accuracy is achieved using beta waves signals of frontal asymmetry channels. The results clearly indicate the potential of detecting motivation states using EEG signals, provided suitable methodologies such as proposed in this paper, are employed.||URI:||https://hdl.handle.net/10356/162728||ISSN:||0957-4174||DOI:||10.1016/j.eswa.2021.115548||Rights:||© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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