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2020 | OriginalPaper | Chapter

Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network

Authors : Md. Shahriare Satu, Shelia Rahman, Md. Imran Khan, Mohammad Zoynul Abedin, M. Shamim Kaiser, Mufti Mahmud

Published in: Brain Informatics

Publisher: Springer International Publishing

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Abstract

Cognitive performance dictates how an individual perceives, records, maintains, retrieves, manipulates, uses and expresses information and are provided in any task that the person is involved in, let it be from the simplest to the most complex. Therefore, it is imperative to identify how a person is cognitively engaging specially in tasks such as information acquisition and studying. Given the surge in online education system, this even becomes more important as the visual feedback of student engagement is missing from the loop. To address this issue, the current study proposes a pipeline to detect cognitive performance by analyzing electroencephalogram (EEG) signals using bidirectional multilayer long-short term memory (BML-LSTM). Tested on an EEG brainwave dataset from 10 students while they watched massive open online course video clips, the obtained results using BML-LSTM show an accuracy \({>}95\%\) in detecting cognitive performance which outperforms all previous methods applied on the same dataset.

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Metadata
Title
Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network
Authors
Md. Shahriare Satu
Shelia Rahman
Md. Imran Khan
Mohammad Zoynul Abedin
M. Shamim Kaiser
Mufti Mahmud
Copyright Year
2020
Book
Brain Informatics

Print ISBN: 978-3-030-59276-9

Electronic ISBN: 978-3-030-59277-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-59277-6_27

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