Detecting student attention through electroencephalography signals: a comparative analysis of deep learning models Article Swipe
Eng Lye Lim
,
Raja Kumar Murugesan
,
Sumathi Balakrishnan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.11591/ijai.v13.i4.pp4608-4618
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.11591/ijai.v13.i4.pp4608-4618
In the landscape of educational technology, understanding and optimizing student attention is important to enhance student’s learning experience. This study explores the potential of using electroencephalography (EEG) signals for discerning students' attention levels during educational tasks. With a cohort of 30 participants, EEG data were meticulously collected and subjected to robust preprocessing techniques, including independent component analysis (ICA) and principal component analysis (PCA). The research then employed different deep learning algorithm such as long short-term memory (LSTM), recurrent neural network (RNN), gated recurrent unit (GRU), multi-layer perceptron (MLP), and convolutional neural network (CNN) classifiers to predict students' attention. The results reveal notable variations in the classifiers' predictive performance. Our finding revealed that the LSTM model emerged as the top performer and achieved 96% of the accuracy. This study not only contributes to the advancement of attention detection in educational technology but also underscores the importance of preprocessing methodologies, such as ICA and PCA, in optimizing the performance of deep learning models for EEG-based applications.
Related Topics
Concepts
Computer science
Electroencephalography
Artificial intelligence
Deep learning
Convolutional neural network
Principal component analysis
Preprocessor
Independent component analysis
Machine learning
Recurrent neural network
Multilayer perceptron
Perceptron
Artificial neural network
Pattern recognition (psychology)
Speech recognition
Psychology
Psychiatry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/ijai.v13.i4.pp4608-4618
- OA Status
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- 10
- OpenAlex ID
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https://openalex.org/W4403209909Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.11591/ijai.v13.i4.pp4608-4618Digital Object Identifier
- Title
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Detecting student attention through electroencephalography signals: a comparative analysis of deep learning modelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-08Full publication date if available
- Authors
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Eng Lye Lim, Raja Kumar Murugesan, Sumathi BalakrishnanList of authors in order
- Landing page
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https://doi.org/10.11591/ijai.v13.i4.pp4608-4618Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.11591/ijai.v13.i4.pp4608-4618Direct OA link when available
- Concepts
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Computer science, Electroencephalography, Artificial intelligence, Deep learning, Convolutional neural network, Principal component analysis, Preprocessor, Independent component analysis, Machine learning, Recurrent neural network, Multilayer perceptron, Perceptron, Artificial neural network, Pattern recognition (psychology), Speech recognition, Psychology, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.applications.</span> | 164 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.21954542 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |