Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress
Due to Covid-19 pandemics over the past few years, schools are closed and classes are conducted online, companies force their employee to work from home due to government Covid-19 policy. Therefore many people are stuck at home doing their work or studying facing the four walls of their home or bedr...
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2022
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sg-ntu-dr.10356-1581752023-07-07T19:27:20Z Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress Lee, Zi Min Yvonne Lam Ying Hung School of Electrical and Electronic Engineering EYHLAM@ntu.edu.sg Engineering::Electrical and electronic engineering Due to Covid-19 pandemics over the past few years, schools are closed and classes are conducted online, companies force their employee to work from home due to government Covid-19 policy. Therefore many people are stuck at home doing their work or studying facing the four walls of their home or bedroom. They have a lesser chance to head out to exercise, interact with people or even enjoy a meal with their close ones. People start to not take care of themselves and neglect their mental wellbeing. As a result mental well-being declines and stress increases. Hence a proposal of designing a smart control system using brainwave signals extracted from Myndplay's brainband to detect stress will help people to be more aware of their own stress and mental wellbeing. At the same time, they will be more aware of taking action to counter their stress and this will help them improve their quality of life. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-27T06:37:06Z 2022-05-27T06:37:06Z 2022 Final Year Project (FYP) Lee, Z. M. (2022). Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158175 https://hdl.handle.net/10356/158175 en A2270-211 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Lee, Zi Min Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress |
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Due to Covid-19 pandemics over the past few years, schools are closed and classes are conducted online, companies force their employee to work from home due to government Covid-19 policy. Therefore many people are stuck at home doing their work or studying facing the four walls of their home or bedroom. They have a lesser chance to head out to exercise, interact with people or even enjoy a meal with their close ones. People start to not take care of themselves and neglect their mental wellbeing. As a result mental well-being declines and stress increases.
Hence a proposal of designing a smart control system using brainwave signals extracted from Myndplay's brainband to detect stress will help people to be more aware of their own stress and mental wellbeing. At the same time, they will be more aware of taking action to counter their stress and this will help them improve their quality of life. |
author2 |
Yvonne Lam Ying Hung |
author_facet |
Yvonne Lam Ying Hung Lee, Zi Min |
format |
Final Year Project |
author |
Lee, Zi Min |
author_sort |
Lee, Zi Min |
title |
Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress |
title_short |
Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress |
title_full |
Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress |
title_fullStr |
Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress |
title_full_unstemmed |
Design a smart control system using brainwave signals extracted from myndplay brainband to detect stress |
title_sort |
design a smart control system using brainwave signals extracted from myndplay brainband to detect stress |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/158175 |
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1772825167153594368 |