Artificial intelligence based analysis and game control using brain signals
Brain Computer Interface is an emerging field which is gaining traction particularly in the use for video games. This is done by extracting brain signals via a conventional method used in the biomedical domain, which is Electroencephalography(EEG). The project involves developing the entire pipeline...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156470 |
| _version_ | 1811689905182474240 |
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| author | Hong, Calvin Wen Kit |
| author2 | Smitha Kavallur Pisharath Gopi |
| author_facet | Smitha Kavallur Pisharath Gopi Hong, Calvin Wen Kit |
| author_sort | Hong, Calvin Wen Kit |
| collection | NTU |
| description | Brain Computer Interface is an emerging field which is gaining traction particularly in the use for video games. This is done by extracting brain signals via a conventional method used in the biomedical domain, which is Electroencephalography(EEG). The project involves developing the entire pipeline of the signal extraction, processing, classification, game development and interfacing. A low cost, low electrode count EEG capturing device, known as the InterAxon Muse 2, is used as the choice of hardware for this project. This project also aims to evaluate the feasibility of using the InterAxon Muse 2 as the EEG capturing choice.
In particular, this project has two main points to evaluate; the first is to compare and contrast the separability of attentive and relax states, and the second is to evaluate whether the model is transferable onto a game. Experiments were conducted using various neurocognitive prompts such as the flanker task and the proposed majority task with varying block length. The results are visualized using Principle Component Analysis and are trained on SVM and Neural Network models. A simple ball game, created using Unity3D is used in tandem with the trained models to evaluate the transferability of
the model for the game.
Results reflect that Neural Networks have a higher performance as compared to SVMs, and that the proposed majority task has a higher classification accuracy based on the mean obtained from the 10 subjects, and that the models are generally transferable onto the ball game created. |
| first_indexed | 2024-10-01T05:55:31Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/156470 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:55:31Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1564702022-04-17T11:34:27Z Artificial intelligence based analysis and game control using brain signals Hong, Calvin Wen Kit Smitha Kavallur Pisharath Gopi School of Computer Science and Engineering smitha@ntu.edu.sg Science::Medicine::Biosensors Engineering::Computer science and engineering Brain Computer Interface is an emerging field which is gaining traction particularly in the use for video games. This is done by extracting brain signals via a conventional method used in the biomedical domain, which is Electroencephalography(EEG). The project involves developing the entire pipeline of the signal extraction, processing, classification, game development and interfacing. A low cost, low electrode count EEG capturing device, known as the InterAxon Muse 2, is used as the choice of hardware for this project. This project also aims to evaluate the feasibility of using the InterAxon Muse 2 as the EEG capturing choice. In particular, this project has two main points to evaluate; the first is to compare and contrast the separability of attentive and relax states, and the second is to evaluate whether the model is transferable onto a game. Experiments were conducted using various neurocognitive prompts such as the flanker task and the proposed majority task with varying block length. The results are visualized using Principle Component Analysis and are trained on SVM and Neural Network models. A simple ball game, created using Unity3D is used in tandem with the trained models to evaluate the transferability of the model for the game. Results reflect that Neural Networks have a higher performance as compared to SVMs, and that the proposed majority task has a higher classification accuracy based on the mean obtained from the 10 subjects, and that the models are generally transferable onto the ball game created. Bachelor of Engineering (Computer Science) 2022-04-17T11:34:27Z 2022-04-17T11:34:27Z 2022 Final Year Project (FYP) Hong, C. W. K. (2022). Artificial intelligence based analysis and game control using brain signals. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156470 https://hdl.handle.net/10356/156470 en application/pdf Nanyang Technological University |
| spellingShingle | Science::Medicine::Biosensors Engineering::Computer science and engineering Hong, Calvin Wen Kit Artificial intelligence based analysis and game control using brain signals |
| title | Artificial intelligence based analysis and game control using brain signals |
| title_full | Artificial intelligence based analysis and game control using brain signals |
| title_fullStr | Artificial intelligence based analysis and game control using brain signals |
| title_full_unstemmed | Artificial intelligence based analysis and game control using brain signals |
| title_short | Artificial intelligence based analysis and game control using brain signals |
| title_sort | artificial intelligence based analysis and game control using brain signals |
| topic | Science::Medicine::Biosensors Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/156470 |
| work_keys_str_mv | AT hongcalvinwenkit artificialintelligencebasedanalysisandgamecontrolusingbrainsignals |