Tiwari Purnendu, Ghosh Subhojit, Sinha Rakesh Kumar
Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India.
M. Tech., Computer Technology, National Institute of Technology, Raipur 492001, India.
Comput Intell Neurosci. 2015;2015:945729. doi: 10.1155/2015/945729. Epub 2015 Apr 20.
Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.
将脑机接口（BCI）从实验室环境转移到满足现实世界应用需求，需要BCI在没有任何时间限制的情况下异步应用。脑电图（EEG）信号的高度动态性促使我们关注进化算法（EA）。受这两个事实的启发，在这项工作中，一种基于遗传算法-粒子群优化（GA-PSO）的混合K均值聚类技术被用于区分两类运动想象（MI）任务。结果发现，所提出的基于GA-PSO的混合K均值聚类在准确性和执行时间方面均优于基于遗传算法（GA）和粒子群优化（PSO）的K均值聚类技术。混合GA-PSO技术较短的执行时间使其适用于实时BCI应用。时频表示（TFR）技术已被用于提取所研究信号的特征。基于TFR提取特征，并依靠事件相关同步（ERD）和去同步（ERS）的概念形成特征向量。
