The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning
In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this pap...
| Main Authors: | , , , , , , |
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| Format: | Article |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/5/1613 |
| _version_ | 1827588317038772224 |
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| author | Man Li Feng Li Jiahui Pan Dengyong Zhang Suna Zhao Jingcong Li Fei Wang |
| author_facet | Man Li Feng Li Jiahui Pan Dengyong Zhang Suna Zhao Jingcong Li Fei Wang |
| author_sort | Man Li |
| collection | DOAJ |
| description | In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games. |
| first_indexed | 2024-03-09T00:32:43Z |
| format | Article |
| id | doaj.art-a4d12f2cdc664c1fb79bc5db970d247a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:32:43Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a4d12f2cdc664c1fb79bc5db970d247a2023-12-11T18:25:27ZengMDPI AGSensors1424-82202021-02-01215161310.3390/s21051613The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep LearningMan Li0Feng Li1Jiahui Pan2Dengyong Zhang3Suna Zhao4Jingcong Li5Fei Wang6School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaCollege of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaSchool of Software, South China Normal University, Guangzhou 510631, ChinaIn addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.https://www.mdpi.com/1424-8220/21/5/1613brain–computer interface (BCI)electroencephalogram (EEG)P300BCI gameBayesian deep learning |
| spellingShingle | Man Li Feng Li Jiahui Pan Dengyong Zhang Suna Zhao Jingcong Li Fei Wang The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning Sensors brain–computer interface (BCI) electroencephalogram (EEG) P300 BCI game Bayesian deep learning |
| title | The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning |
| title_full | The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning |
| title_fullStr | The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning |
| title_full_unstemmed | The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning |
| title_short | The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning |
| title_sort | mindgomoku an online p300 bci game based on bayesian deep learning |
| topic | brain–computer interface (BCI) electroencephalogram (EEG) P300 BCI game Bayesian deep learning |
| url | https://www.mdpi.com/1424-8220/21/5/1613 |
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