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.

中文翻译：

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基于贝叶斯深度学习的在线 P300 BCI 游戏


除了帮助开发帮助残疾人的产品外，脑机接口（BCI）技术还可以成为所有人的娱乐方式。然而，大多数BCI游戏由于控制性能较差或容易产生疲劳而无法广泛推广。在本文中，我们提出了一种 P300 脑机接口游戏（MindGomoku），以探索在实际环境中利用脑电图（EEG）信号玩游戏的可行且自然的方式。本研究的新颖性体现在设计BCI游戏和范式时融合游戏规则和BCI系统的特点。此外，引入了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。为了证明所提出的算法和系统控制的可靠性，选择了10名受试者参加两次在线控制实验。实验结果显示，所有受试者均顺利完成游戏控制，平均准确率达90.7%，平均下棋时间超过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。这种BCI系统不仅为用户，特别是残疾人提供了一种娱乐方式，也为游戏提供了更多的可能性。