Brain-Machine Interface for Developing Virtual-Ball Movement Controlling Game
Brain-Machine Interface for Developing Virtual-Ball Movement Controlling Game
复制标题
用于开发虚拟球运动控制游戏的脑机接口
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
D. Farid
中科院分区：
文献类型：
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作者：
Md. Ochiuddin Miah;Al Maruf Hassan;K. Mamun;D. Farid
用于生物信号处理和建模的智能系统是一种用于创建信号以测量大脑活动以通过外部设备执行任务的方法。脑-机接口（Brain-Machine Interface，BMI），又称脑-机接口（Brain-Computer Interface，BCI）、神经控制接口（Neural Control Interface，NCI）、心智-机器接口（Mind-Machine Interface，MMI）、直接神经接口（Direct Neural Interface，DNI），是脑与机器之间的直接通信途径。近年来，计算模型研究者们正在应用BMI技术来探索先进的知识，以发现生物学的基本问题。在本文中，我们探索了BMI技术，并开发了一个系统，可以区分人类的思想。最初，我们获得了大脑信号并从这些信号中提取特征来构建训练和测试数据。我们设计了二类和三类分类器，采用OneR，朴素贝叶斯（NB）分类器，决策树（DT）归纳，随机森林和Bagging分类器。随机森林在二类和三类分类中分别达到了93.16%和62.84%的准确率。相反，决策树（C4.5）分类器实现了90.89%和65.66%的二类和三类分类的准确率。然后，我们已经考虑了整体性能和决策树分类器开发的交互式游戏，可以通过脑机接口操作，而无需与计算机进行物理交互。
Intelligent Systems for bio-signals processing and modeling are a method for creating signals to measure the brain activities to perform a task by an external device. Brain–Machine Interface (BMI) that is also known as Brain–Computer Interface (BCI), Neural Control Interface (NCI), Mind–Machine Interface (MMI), and Direct Neural Interface (DNI) is a direct communication pathway between brain and machine. Recently, computational modeling researchers are applying BMI techniques to explore advanced knowledge for discovering biological fundamental problems. In this paper, we have explored BMI techniques and developed a system that can distinguish human thoughts. Initially, we have obtained the brain signals and extracted features from these signals to build training and test data. We have designed binary-class and three-class classifiers by employing OneR, naive Bayes (NB) classifier, decision tree (DT) induction, Random Forest, and Bagging classifiers. Random Forest achieved 93.16 and 62.84% accuracy for binary-class and three-class classification. On the contrary, decision tree (C4.5) classifier achieved 90.89 and 65.66% accuracy for binary-class and three-class classification. Then we have considered overall performance and applied decision tree classifier for developing an interactive game that can operate through brain–machine interface without physical interaction with the computer.