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short-paper

Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks

Authors:

Ahmet Cem Yuksel,

Michael I. Mandel,

Lei XieAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 241 - 246

https://doi.org/10.1145/3107411.3107513

Published: 20 August 2017 Publication History

Abstract

Brain fog, also known as confusion, is one of the main reasons of the low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in human's mind in real time is a challenging and important task which can be applied to online education, driver fatigue detection and so on. In this paper, we applied Bidirectional LSTM Recurrent Neural Networks to classify students' confusions. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activities.

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Cited By

Ganepola DMaduranga MTilwari VKarunaratne I(2024)A Systematic Review of Electroencephalography-Based Emotion Recognition of Confusion Using Artificial IntelligenceSignals10.3390/signals50200135:2(244-263)Online publication date: 25-Apr-2024
https://doi.org/10.3390/signals5020013
Saidani OUmer MAlshardan AAlturki NNappi MAshraf I(2024)Student academic success prediction in multimedia-supported virtual learning system using ensemble learning approachMultimedia Tools and Applications10.1007/s11042-024-18669-z83:40(87553-87578)Online publication date: 18-Mar-2024
https://doi.org/10.1007/s11042-024-18669-z
Tahir HJung E(2023)Comparative Study on Distributed Lightweight Deep Learning Models for Road Pothole DetectionSensors10.3390/s2309434723:9(4347)Online publication date: 27-Apr-2023
https://doi.org/10.3390/s23094347
Show More Cited By

Index Terms

Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection
    2. Machine learning approaches
      1. Neural networks

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Information

Published In

cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

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U.S. National Library of Medicine

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BCB '17

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SIGBio

BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

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ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Ganepola DMaduranga MTilwari VKarunaratne I(2024)A Systematic Review of Electroencephalography-Based Emotion Recognition of Confusion Using Artificial IntelligenceSignals10.3390/signals50200135:2(244-263)Online publication date: 25-Apr-2024
https://doi.org/10.3390/signals5020013
Saidani OUmer MAlshardan AAlturki NNappi MAshraf I(2024)Student academic success prediction in multimedia-supported virtual learning system using ensemble learning approachMultimedia Tools and Applications10.1007/s11042-024-18669-z83:40(87553-87578)Online publication date: 18-Mar-2024
https://doi.org/10.1007/s11042-024-18669-z
Tahir HJung E(2023)Comparative Study on Distributed Lightweight Deep Learning Models for Road Pothole DetectionSensors10.3390/s2309434723:9(4347)Online publication date: 27-Apr-2023
https://doi.org/10.3390/s23094347
Walther DViehweg JHaueisen JMäder P(2023)A systematic comparison of deep learning methods for EEG time series analysisFrontiers in Neuroinformatics10.3389/fninf.2023.106709517Online publication date: 23-Feb-2023
https://doi.org/10.3389/fninf.2023.1067095
Abuzinadah NUmer MIshaq AAl Hejaili AAlsubai SEshmawi AMohamed AAshraf I(2023)Role of convolutional features and machine learning for predicting student academic performance from MOODLE dataPLOS ONE10.1371/journal.pone.029306118:11(e0293061)Online publication date: 8-Nov-2023
https://doi.org/10.1371/journal.pone.0293061
ESPÍNDOLA-ULIBARRI LACEVEDO-MOSQUEDA MACEVEDO-MOSQUEDA MGÓMEZ-CORONEL SCARREÑO-AGUILERA R(2023)DIAGNOSIS OF STUDENT CONFUSION THROUGH ARTIFICIAL INTELLIGENCEFractals10.1142/S0218348X2450010532:01Online publication date: 29-Nov-2023
https://doi.org/10.1142/S0218348X24500105
Kopparapu SBhattacharya DSinha N(2023)Spatial Encoding of EEG Brain Wave Signals to Predict Student’s Mental State During E-Learning2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)10.1109/MLSP55844.2023.10285955(1-6)Online publication date: 17-Sep-2023
https://doi.org/10.1109/MLSP55844.2023.10285955
Gupta RKumar J(2023)Uncovering of learner’s confusion using EEG Data during online learning2023 5th Biennial International Conference on Nascent Technologies in Engineering (ICNTE)10.1109/ICNTE56631.2023.10146659(1-5)Online publication date: 20-Jan-2023
https://doi.org/10.1109/ICNTE56631.2023.10146659
Ammar HShrara HNassereddine MCharara JSbeity F(2023)An EEG Study of the Student's Confusion Using Deep Learning2023 Seventh International Conference on Advances in Biomedical Engineering (ICABME)10.1109/ICABME59496.2023.10293046(121-124)Online publication date: 12-Oct-2023
https://doi.org/10.1109/ICABME59496.2023.10293046
Ganiga RKim YTulluri RChoi W(2023)Modeling EEG Signals for Mental Confusion Using DNN and LSTM With Custom Attention LayerIEEE Access10.1109/ACCESS.2023.333703511(134663-134676)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3337035
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