Application of machine learning in attentiveness detection from EEG signal
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2023
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| Rochtain ar líne: | http://hdl.handle.net/10361/21791 |
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10361-217912023-10-12T21:03:24Z Application of machine learning in attentiveness detection from EEG signal Ridi, Sadia Sobhana Tandra, Jannatul Farzana Emon, Mahmudul Hasan Mahmud, Md Ridwan Tabassum, Sumaiya Ashraf, Faisal Bin Department of Computer Science and Engineering, Brac University Textual representation Brain signal BCI EEG LRR CNN ERP Brain--Computer simulation Behavioral assessment--Data processing This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-31). Brain-computer interface (BCI) spellers enable severely motor-impaired people to communicate through brain activity without the use of their muscles. Our brains precisely predict what we will think. If a human-readable character can be identified by its appearance, our issues may be resolved. Currently, human, machine, and brain communication based on machine learning is highly believable. In this study, we intend to employ the non-invasive brain stimulation technique, often known as EEG, for the treatment of these individuals. A Braincomputer interface system based on electroencephalography provides the optimal solution to this issue. It establishes a link between the brain and the computer system, allowing brain waves to control our actions. The objective is to determine if a person is paying attention by recognizing characters from a dataset of P300, which is an event-related potential (ERP) component, using a BCI design. If a character is identified as a person paying attention, the data is labelled as target class; otherwise, the data is displayed as non-target. Our study has resulted in a number of Machine Learning strategy techniques. In this study, we analyzed the performance of four different types of Machine Learning Algorithms, including Logistic Regression (LRR), Random Forest Classifier, AdaBoost classifier, and XGBoost Classifier, to determine the most accurate algorithm. Custom CNN achieved the highest accuracy among classifiers, at approximately 88.46%. Sadia Sobhana Ridi Jannatul Farzana Tandra Mahmudul Hasan Emon Md Ridwan Mahmud Sumaiya Tabassum B.Sc. in Computer Science and Engineering 2023-10-12T09:07:21Z 2023-10-12T09:07:21Z ©2022 2022-09-29 Thesis ID 18301279 ID 19101097 ID 19101098 ID 19101104 ID 19101113 http://hdl.handle.net/10361/21791 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 42 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Textual representation Brain signal BCI EEG LRR CNN ERP Brain--Computer simulation Behavioral assessment--Data processing |
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Textual representation Brain signal BCI EEG LRR CNN ERP Brain--Computer simulation Behavioral assessment--Data processing Ridi, Sadia Sobhana Tandra, Jannatul Farzana Emon, Mahmudul Hasan Mahmud, Md Ridwan Tabassum, Sumaiya Application of machine learning in attentiveness detection from EEG signal |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Ashraf, Faisal Bin |
| author_facet |
Ashraf, Faisal Bin Ridi, Sadia Sobhana Tandra, Jannatul Farzana Emon, Mahmudul Hasan Mahmud, Md Ridwan Tabassum, Sumaiya |
| format |
Thesis |
| author |
Ridi, Sadia Sobhana Tandra, Jannatul Farzana Emon, Mahmudul Hasan Mahmud, Md Ridwan Tabassum, Sumaiya |
| author_sort |
Ridi, Sadia Sobhana |
| title |
Application of machine learning in attentiveness detection from EEG signal |
| title_short |
Application of machine learning in attentiveness detection from EEG signal |
| title_full |
Application of machine learning in attentiveness detection from EEG signal |
| title_fullStr |
Application of machine learning in attentiveness detection from EEG signal |
| title_full_unstemmed |
Application of machine learning in attentiveness detection from EEG signal |
| title_sort |
application of machine learning in attentiveness detection from eeg signal |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21791 |
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