Motor imagery EEG-based game using Emotiv EPOC+
Electroencephalography (EEG) is the process of monitoring the electrical activities of the brain for various recording and diagnostic purposes. Apart from that, the signals derived can also be used as a control mechanism for video gaming through Brain-Computer Interfacing (BCI). This project involve...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/144508 |
| _version_ | 1811689291086036992 |
|---|---|
| author | Sim, Gerald Tong |
| author2 | Smitha Kavallur Pisharath Gopi |
| author_facet | Smitha Kavallur Pisharath Gopi Sim, Gerald Tong |
| author_sort | Sim, Gerald Tong |
| collection | NTU |
| description | Electroencephalography (EEG) is the process of monitoring the electrical activities of the brain for various recording and diagnostic purposes. Apart from that, the signals derived can also be used as a control mechanism for video gaming through Brain-Computer Interfacing (BCI). This project involves the development of an infinite runner-style Unity 3D game, Mental Drive, that makes use of Motor Imagery (MI) signals acquired from an Emotiv EPOC+ headset as an active control mechanism in the game. By utilizing a relatively low-cost EEG acquisition device compared to research- and medical-grade EEG devices, and designing the game around stroke patients and stroke-vulnerable elderly, the project aims to explore the possibility of using MI EEG-based games alongside low-cost acquisition hardware to assist in stroke rehabilitation in the community. To evaluate the classification accuracy of the MI signals from the Emotiv EPOC+, two experiments using the Mental Drive game were conducted with eight participants consisting of stroke patients and healthy adults. One experiment conducted involved wearing the Emotiv EPOC+ according to the manufacturer’s specification, while another experiment conducted involved wearing the Emotiv EPOC+ in a novel unconventional manner. The experiment results reflect that the novel manner of wearing of the Emotiv EPOC+ was able to produce higher MI signals classification accuracy for the game controls as compared to the manufacturer specified manner. The results imply that further research and experimentations can be done with a larger population to explore MI EEG-based games using low-cost hardware for stroke rehabilitation. |
| first_indexed | 2024-10-01T05:45:46Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/144508 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:45:46Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1445082020-11-10T06:30:31Z Motor imagery EEG-based game using Emotiv EPOC+ Sim, Gerald Tong Smitha Kavallur Pisharath Gopi School of Computer Science and Engineering smitha@ntu.edu.sg Engineering::Computer science and engineering::Software::Software engineering Electroencephalography (EEG) is the process of monitoring the electrical activities of the brain for various recording and diagnostic purposes. Apart from that, the signals derived can also be used as a control mechanism for video gaming through Brain-Computer Interfacing (BCI). This project involves the development of an infinite runner-style Unity 3D game, Mental Drive, that makes use of Motor Imagery (MI) signals acquired from an Emotiv EPOC+ headset as an active control mechanism in the game. By utilizing a relatively low-cost EEG acquisition device compared to research- and medical-grade EEG devices, and designing the game around stroke patients and stroke-vulnerable elderly, the project aims to explore the possibility of using MI EEG-based games alongside low-cost acquisition hardware to assist in stroke rehabilitation in the community. To evaluate the classification accuracy of the MI signals from the Emotiv EPOC+, two experiments using the Mental Drive game were conducted with eight participants consisting of stroke patients and healthy adults. One experiment conducted involved wearing the Emotiv EPOC+ according to the manufacturer’s specification, while another experiment conducted involved wearing the Emotiv EPOC+ in a novel unconventional manner. The experiment results reflect that the novel manner of wearing of the Emotiv EPOC+ was able to produce higher MI signals classification accuracy for the game controls as compared to the manufacturer specified manner. The results imply that further research and experimentations can be done with a larger population to explore MI EEG-based games using low-cost hardware for stroke rehabilitation. Bachelor of Engineering (Computer Science) 2020-11-10T06:30:31Z 2020-11-10T06:30:31Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/144508 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Software::Software engineering Sim, Gerald Tong Motor imagery EEG-based game using Emotiv EPOC+ |
| title | Motor imagery EEG-based game using Emotiv EPOC+ |
| title_full | Motor imagery EEG-based game using Emotiv EPOC+ |
| title_fullStr | Motor imagery EEG-based game using Emotiv EPOC+ |
| title_full_unstemmed | Motor imagery EEG-based game using Emotiv EPOC+ |
| title_short | Motor imagery EEG-based game using Emotiv EPOC+ |
| title_sort | motor imagery eeg based game using emotiv epoc |
| topic | Engineering::Computer science and engineering::Software::Software engineering |
| url | https://hdl.handle.net/10356/144508 |
| work_keys_str_mv | AT simgeraldtong motorimageryeegbasedgameusingemotivepoc |