A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns
Classroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in...
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2024-11-01
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| Series: | Algorithms |
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| author | Rajamanickam Yuvaraj Shivam Chadha A. Amalin Prince M. Murugappan Md. Sakib Bin Islam Md. Shaheenur Islam Sumon Muhammad E. H. Chowdhury |
| author_facet | Rajamanickam Yuvaraj Shivam Chadha A. Amalin Prince M. Murugappan Md. Sakib Bin Islam Md. Shaheenur Islam Sumon Muhammad E. H. Chowdhury |
| author_sort | Rajamanickam Yuvaraj |
| collection | DOAJ |
| description | Classroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in class, revealing learning preferences. The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. Method: In this study, a set of EEG features was investigated, including statistical features, fractal dimension, higher-order spectra, entropy, and a combination of all sets. Three different machine learning classifiers, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were used to evaluate the performance. The proposed framework was evaluated on the real classroom EEG dataset, involving EEG recordings featuring different teaching blocks: reading, discussion, lecture, and video. <i>Results:</i> The findings revealed that statistical features are the most sensitive feature metric in distinguishing learning patterns from EEG. The statistical features and RF classifier method tested in this study achieved an overall best average accuracy of 78.45% when estimated by fivefold cross-validation. Conclusions: Our results suggest that EEG time domain statistics have a substantial role and are more reliable for internal state classification. This study might be used to highlight the importance of using EEG signals in the education context, opening the path for educational automation research and development. |
| format | Article |
| id | doaj-art-ee0cbfb128d44d38bc548dabca5a80f9 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-ee0cbfb128d44d38bc548dabca5a80f92024-11-26T17:45:26ZengMDPI AGAlgorithms1999-48932024-11-01171150310.3390/a17110503A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style PatternsRajamanickam Yuvaraj0Shivam Chadha1A. Amalin Prince2M. Murugappan3Md. Sakib Bin Islam4Md. Shaheenur Islam Sumon5Muhammad E. H. Chowdhury6Office of Education Research, Science of Learning in Education Centre (SoLEC), National Institute of Education (NIE), Nanyang Technological University (NTU), Nanyang Walk, Singapore 637616, SingaporeDepartment of Electrical and Electronics Engineering, BITS Pilani K k Birla Goa Campus, Sancoale 403726, Goa, IndiaDepartment of Electrical and Electronics Engineering, BITS Pilani K k Birla Goa Campus, Sancoale 403726, Goa, IndiaIntelligent Signal Processing (ISP) Research Laboratory, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, KuwaitDepartment of Electrical Engineering, Qatar University, Doha 2713, QatarDepartment of Electrical Engineering, Qatar University, Doha 2713, QatarDepartment of Electrical Engineering, Qatar University, Doha 2713, QatarClassroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in class, revealing learning preferences. The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. Method: In this study, a set of EEG features was investigated, including statistical features, fractal dimension, higher-order spectra, entropy, and a combination of all sets. Three different machine learning classifiers, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were used to evaluate the performance. The proposed framework was evaluated on the real classroom EEG dataset, involving EEG recordings featuring different teaching blocks: reading, discussion, lecture, and video. <i>Results:</i> The findings revealed that statistical features are the most sensitive feature metric in distinguishing learning patterns from EEG. The statistical features and RF classifier method tested in this study achieved an overall best average accuracy of 78.45% when estimated by fivefold cross-validation. Conclusions: Our results suggest that EEG time domain statistics have a substantial role and are more reliable for internal state classification. This study might be used to highlight the importance of using EEG signals in the education context, opening the path for educational automation research and development.https://www.mdpi.com/1999-4893/17/11/503classroom EEGacademiclearning stylecross-validationstatistical measurementsautomation |
| spellingShingle | Rajamanickam Yuvaraj Shivam Chadha A. Amalin Prince M. Murugappan Md. Sakib Bin Islam Md. Shaheenur Islam Sumon Muhammad E. H. Chowdhury A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns Algorithms classroom EEG academic learning style cross-validation statistical measurements automation |
| title | A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns |
| title_full | A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns |
| title_fullStr | A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns |
| title_full_unstemmed | A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns |
| title_short | A Machine Learning Framework for Classroom EEG Recording Classification: Unveiling Learning-Style Patterns |
| title_sort | machine learning framework for classroom eeg recording classification unveiling learning style patterns |
| topic | classroom EEG academic learning style cross-validation statistical measurements automation |
| url | https://www.mdpi.com/1999-4893/17/11/503 |
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