Li Man, Li Feng, Pan Jiahui, Zhang Dengyong, Zhao Suna, Li Jingcong, Wang Fei
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China.
Sensors (Basel). 2021 Feb 25;21(5):1613. doi: 10.3390/s21051613.
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.
除了帮助开发辅助残疾人的产品外，脑机接口（BCI）技术还可以成为所有人的一种娱乐方式。然而，由于控制性能不佳或容易导致疲劳，大多数BCI游戏无法得到广泛推广。在本文中，我们提出了一种P300脑机接口游戏（MindGomoku），以探索一种在实际环境中使用脑电图（EEG）信号玩游戏的可行且自然的方式。这项研究的新颖之处在于在设计BCI游戏和范式时整合了游戏规则和BCI系统的特点。此外，引入了一种简化的贝叶斯卷积神经网络（SBCNN）算法，以在有限的训练样本上实现高精度。为了证明所提出算法和系统控制的可靠性，选择了10名受试者参加两个在线控制实验。实验结果表明，所有受试者均成功完成了游戏控制，平均准确率为90.7%，平均玩MindGomoku的时间超过11分钟。这些发现充分证明了所提出系统的稳定性和有效性。该BCI系统不仅为用户，特别是残疾人提供了一种娱乐形式，也为游戏提供了更多可能性。
