A portable cognitive tool for engagement level and activity identification
Wearable devices such as Electroencephalography (EEG) hold immense potential in the monitoring and assessment of a person’s task engagement. This is especially so in remote or online sites. Research into its use in measuring an individual's cognitive state while performing task activities is th...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/173503 https://icecce.com/ |
| _version_ | 1826118446678540288 |
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| author | Teo, Terry Lye, Sun Woh Li, Yu Fei Zakaria, Zainuddin |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Teo, Terry Lye, Sun Woh Li, Yu Fei Zakaria, Zainuddin |
| author_sort | Teo, Terry |
| collection | NTU |
| description | Wearable devices such as Electroencephalography (EEG) hold immense potential in the monitoring and assessment of a person’s task engagement. This is especially so in remote or online sites. Research into its use in measuring an individual's cognitive state while performing task activities is therefore expected to increase. Despite the growing number of EEG research into brain functioning activities of a person, key challenges remain in adopting EEG for real-time operations. These include limited portability, long preparation time, high number of channel dimensionality, intrusiveness, as well as level of accuracy in acquiring neurological data. This paper proposes an approach using a 4-6 EEG channels to determine the cognitive states of a subject when undertaking a set of passive and active monitoring tasks of a subject. Air traffic controller (ATC) dynamic-tasks are used as a proxy. The work found that using a newly developed channel reduction and identifier algorithm, good trend adherence of 89.1% can be obtained between a commercially available BCI 14 channel Emotiv EPOC+ EEG headset and that of a carefully selected set of reduced 4-6 channels. The approach can also identify different levels of engagement activities ranging from general monitoring, ad hoc and repeated active monitoring activities involving information search, extraction, and memory activities. |
| first_indexed | 2024-10-01T04:43:52Z |
| format | Conference Paper |
| id | ntu-10356/173503 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:43:52Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1735032024-07-22T02:55:25Z A portable cognitive tool for engagement level and activity identification Teo, Terry Lye, Sun Woh Li, Yu Fei Zakaria, Zainuddin School of Mechanical and Aerospace Engineering 4th International Conference on Electrical, Communication and Computer Engineering (ICECCE 2023) Air Traffic Management Research Institute Engineering Assessment Neurophysiology Monitoring EEG Outliers Wearable devices such as Electroencephalography (EEG) hold immense potential in the monitoring and assessment of a person’s task engagement. This is especially so in remote or online sites. Research into its use in measuring an individual's cognitive state while performing task activities is therefore expected to increase. Despite the growing number of EEG research into brain functioning activities of a person, key challenges remain in adopting EEG for real-time operations. These include limited portability, long preparation time, high number of channel dimensionality, intrusiveness, as well as level of accuracy in acquiring neurological data. This paper proposes an approach using a 4-6 EEG channels to determine the cognitive states of a subject when undertaking a set of passive and active monitoring tasks of a subject. Air traffic controller (ATC) dynamic-tasks are used as a proxy. The work found that using a newly developed channel reduction and identifier algorithm, good trend adherence of 89.1% can be obtained between a commercially available BCI 14 channel Emotiv EPOC+ EEG headset and that of a carefully selected set of reduced 4-6 channels. The approach can also identify different levels of engagement activities ranging from general monitoring, ad hoc and repeated active monitoring activities involving information search, extraction, and memory activities. Civil Aviation Authority of Singapore (CAAS) Submitted/Accepted version This research is supported by the Civil Aviation Authority of Singapore, Workforce Development Applied Research Fund, and Nanyang Technological University, Singapore under their collaboration with the Air Traffic Management Research Institute. 2024-02-08T08:36:37Z 2024-02-08T08:36:37Z 2023 Conference Paper Teo, T., Lye, S. W., Li, Y. F. & Zakaria, Z. (2023). A portable cognitive tool for engagement level and activity identification. 4th International Conference on Electrical, Communication and Computer Engineering (ICECCE 2023). https://hdl.handle.net/10356/173503 https://icecce.com/ en © 2023 ICECCE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. application/pdf |
| spellingShingle | Engineering Assessment Neurophysiology Monitoring EEG Outliers Teo, Terry Lye, Sun Woh Li, Yu Fei Zakaria, Zainuddin A portable cognitive tool for engagement level and activity identification |
| title | A portable cognitive tool for engagement level and activity identification |
| title_full | A portable cognitive tool for engagement level and activity identification |
| title_fullStr | A portable cognitive tool for engagement level and activity identification |
| title_full_unstemmed | A portable cognitive tool for engagement level and activity identification |
| title_short | A portable cognitive tool for engagement level and activity identification |
| title_sort | portable cognitive tool for engagement level and activity identification |
| topic | Engineering Assessment Neurophysiology Monitoring EEG Outliers |
| url | https://hdl.handle.net/10356/173503 https://icecce.com/ |
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