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

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

doi:10.1007/978-3-030-59277-6_27

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

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Scopus citations

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 in detecting cognitive performance which outperforms all previous methods applied on the same dataset.

Original language	English
Title of host publication	Brain Informatics - 13th International Conference, BI 2020, Proceedings
Editors	Mufti Mahmud, Stefano Vassanelli, M. Shamim Kaiser, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	297-306
Number of pages	10
ISBN (Print)	9783030592769
DOIs	https://doi.org/10.1007/978-3-030-59277-6_27
State	Published - 2020
Externally published	Yes
Event	13th International Conference on Brain Informatics, BI 2020 - Padua, Italy Duration: 19 Sep 2020 → 19 Sep 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12241 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Brain Informatics, BI 2020
Country/Territory	Italy
City	Padua
Period	19/09/20 → 19/09/20

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Classifiers
Cognitive performance
Confused students
EEG signal
Machine learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-59277-6_27

Cite this

Satu, M. S., Rahman, S., Khan, M. I., Abedin, M. Z., Kaiser, M. S., & Mahmud, M. (2020). Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network. In M. Mahmud, S. Vassanelli, M. S. Kaiser, & N. Zhong (Eds.), Brain Informatics - 13th International Conference, BI 2020, Proceedings (pp. 297-306). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12241 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59277-6_27

Satu, Md Shahriare ; Rahman, Shelia ; Khan, Md Imran et al. / Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network. Brain Informatics - 13th International Conference, BI 2020, Proceedings. editor / Mufti Mahmud ; Stefano Vassanelli ; M. Shamim Kaiser ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2020. pp. 297-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 in detecting cognitive performance which outperforms all previous methods applied on the same dataset.",

keywords = "Classifiers, Cognitive performance, Confused students, EEG signal, Machine learning",

author = "Satu, \{Md Shahriare\} and Shelia Rahman and Khan, \{Md Imran\} and Abedin, \{Mohammad Zoynul\} and Kaiser, \{M. Shamim\} and Mufti Mahmud",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 13th International Conference on Brain Informatics, BI 2020 ; Conference date: 19-09-2020 Through 19-09-2020",

year = "2020",

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Satu, MS, Rahman, S, Khan, MI, Abedin, MZ, Kaiser, MS & Mahmud, M 2020, Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network. in M Mahmud, S Vassanelli, MS Kaiser & N Zhong (eds), Brain Informatics - 13th International Conference, BI 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12241 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 297-306, 13th International Conference on Brain Informatics, BI 2020, Padua, Italy, 19/09/20. https://doi.org/10.1007/978-3-030-59277-6_27

Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network. / Satu, Md Shahriare; Rahman, Shelia; Khan, Md Imran et al.
Brain Informatics - 13th International Conference, BI 2020, Proceedings. ed. / Mufti Mahmud; Stefano Vassanelli; M. Shamim Kaiser; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2020. p. 297-306 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12241 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network

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AU - Rahman, Shelia

AU - Khan, Md Imran

AU - Abedin, Mohammad Zoynul

AU - Kaiser, M. Shamim

AU - Mahmud, Mufti

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N2 - 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 in detecting cognitive performance which outperforms all previous methods applied on the same dataset.

AB - 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 in detecting cognitive performance which outperforms all previous methods applied on the same dataset.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Satu MS, Rahman S, Khan MI, Abedin MZ, Kaiser MS, Mahmud M. Towards Improved Detection of Cognitive Performance Using Bidirectional Multilayer Long-Short Term Memory Neural Network. In Mahmud M, Vassanelli S, Kaiser MS, Zhong N, editors, Brain Informatics - 13th International Conference, BI 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 297-306. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59277-6_27

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this