论文标题
计算智能的脑电图学系统接口的设计和实现
Design and Implementation of EEG-Mechatronic System Interface for Computational Intelligence
论文作者
论文摘要
可以读取脑电图（EEG）信号的设备已被广泛用于脑部计算机界面（BCIS）。近年来，随着几种消费级脑电图设备的开发，BCIS领域的受欢迎程度有所提高，这些设备可以实时检测人类认知状态并提供反馈以提高人类绩效。进行了几项研究，以了解BCIS脑电图的基本面和基本方面。但是，如何将消费级脑电图设备有效地使用消费级脑电图设备来有效地受到关注的重大问题。在本文中，我们使用OpenBCI Cyton耳机和运行游戏的用户界面设计并实施了EEG BCI系统。我们采用现实世界的参与者玩游戏来收集训练数据，后来将其放入多个机器学习模型中，包括线性判别分析（LDA），K-Nearest Neighbors（KNN）（KNN）和卷积神经网络（CNN）。在训练机器学习模型之后，实验的验证阶段发生了，参与者试图玩同一游戏，但没有直接控制，利用机器学习模型的输出来确定游戏的移动方式。我们发现，经过针对特定用户的CNN训练游戏，从测试的机器学习模型中执行了最高的激活精度，从而可以通过机电系统系统实现将来实现。
The devices that can read Electroencephalography (EEG) signals have been widely used for Brain-Computer Interfaces (BCIs). Popularity in the field of BCIs has increased in recent years with the development of several consumer-grade EEG devices that can detect human cognitive states in real-time and deliver feedback to enhance human performance. Several studies are conducted to understand the fundamentals and essential aspects of EEG in BCIs. However, the significant issue of how can consumer-grade EEG devices be used to control mechatronic systems effectively has been given less attention. In this paper, we have designed and implemented an EEG BCI system using the OpenBCI Cyton headset and a user interface running a game. We employ real-world participants to play a game to gather training data that was later put into multiple machine learning models, including a linear discriminant analysis (LDA), k-nearest neighbours (KNN), and a convolutional neural network (CNN). After training the machine learning models, a validation phase of the experiment took place where participants tried to play the same game but without direct control, utilising the outputs of the machine learning models to determine how the game moved. We find that a CNN trained to the specific user playing the game performed with the highest activation accuracy from the machine learning models tested, allowing for future implementation with a mechatronic system.
