EEG(brain-wave) for gaming using Emotiv Insight
Technology has improved people's quality of life since the use of modern tools allows industrialists to produce better and higher quality products. Nowadays, several AI algorithms such as machine learning or deep learning are being applied as a tool to solve problems or to make predictions in m...
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sg-ntu-dr.10356-1572792023-07-07T19:00:59Z EEG(brain-wave) for gaming using Emotiv Insight Ng, Ming Sheng Andy Khong W H School of Electrical and Electronic Engineering Centre for Advanced Media Technology AndyKhong@ntu.edu.sg Engineering::Electrical and electronic engineering Technology has improved people's quality of life since the use of modern tools allows industrialists to produce better and higher quality products. Nowadays, several AI algorithms such as machine learning or deep learning are being applied as a tool to solve problems or to make predictions in many different areas. Using Artificial Intelligence, control mechanisms for video gaming through Brain-Computer Interfacing (BCI) are becoming more feasible. It is amazing if people can control their game characters using only their brain without having to use controllers, keyboards, or any other external devices. This paper explores the design, implementation and testing of the BCI-compatible game for children with mobility difficulties, especially children with no functional movement of their hands. A series of mini-games, including "flappy bird", "sunny land" and "dash run", have been developed for PC or mobile (android/iOS) by using Unity Engine. The purpose of this research is to explore ways to use electroencephalography(EEG) brain signals to control the game by using the Emotiv Insight headset. Cortex models are used in Python to read, analyze real-time EEG data, then to extract mental commands and translate them into game commands in C Sharp (C#). EmotivBCI application is being utilized to train mental commands such as PUSH, PULL, LEFT, RIGHT, etc. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-25T01:48:06Z 2022-05-25T01:48:06Z 2022 Final Year Project (FYP) Ng, M. S. (2022). EEG(brain-wave) for gaming using Emotiv Insight. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157279 https://hdl.handle.net/10356/157279 en A3275-211 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Ng, Ming Sheng EEG(brain-wave) for gaming using Emotiv Insight |
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Technology has improved people's quality of life since the use of modern tools allows industrialists to produce better and higher quality products. Nowadays, several AI algorithms such as machine learning or deep learning are being applied as a tool to solve problems or to make predictions in many different areas.
Using Artificial Intelligence, control mechanisms for video gaming through Brain-Computer Interfacing (BCI) are becoming more feasible. It is amazing if people can control their game characters using only their brain without having to use controllers, keyboards, or any other external devices.
This paper explores the design, implementation and testing of the BCI-compatible game for children with mobility difficulties, especially children with no functional movement of their hands. A series of mini-games, including "flappy bird", "sunny land" and "dash run", have been developed for PC or mobile (android/iOS) by using Unity Engine.
The purpose of this research is to explore ways to use electroencephalography(EEG) brain signals to control the game by using the Emotiv Insight headset. Cortex models are used in Python to read, analyze real-time EEG data, then to extract mental commands and translate them into game commands in C Sharp (C#). EmotivBCI application is being utilized to train mental commands such as PUSH, PULL, LEFT, RIGHT, etc. |
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Andy Khong W H |
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Andy Khong W H Ng, Ming Sheng |
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Final Year Project |
author |
Ng, Ming Sheng |
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Ng, Ming Sheng |
title |
EEG(brain-wave) for gaming using Emotiv Insight |
title_short |
EEG(brain-wave) for gaming using Emotiv Insight |
title_full |
EEG(brain-wave) for gaming using Emotiv Insight |
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EEG(brain-wave) for gaming using Emotiv Insight |
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EEG(brain-wave) for gaming using Emotiv Insight |
title_sort |
eeg(brain-wave) for gaming using emotiv insight |
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Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/157279 |
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1772826357518041088 |