Idowu Oluwagbenga Paul, Fang Peng, Li Guanglin
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:519-522. doi: 10.1109/EMBC44109.2020.9176244.
Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.
最近，人们越来越认识到感觉反馈对于正确的运动控制至关重要。借助脑机接口（BCI），运动障碍患者可以通过使用直接从大脑提取的信号与周围环境进行通信或控制周围的事物。广泛使用的基于非侵入性脑电图（EEG）的BCI系统要求首先对脑信号进行预处理，然后将其转换为可转换为外部控制命令的重要特征。由于数据维度高，从采集到的脑信号中确定合适的信息是实现可靠分类准确率的一项重大挑战。特征选择方法是解决此问题的一种可行技术，然而，对于基于运动想象（MI）的BCI，尚未建立一种有效的选择方法来确定能够产生显著分类性能的最佳特征集。本文探讨了蚁群优化算法（ACO）、遗传算法（GA）、布谷鸟搜索算法（CSA）和改进粒子群优化算法（M-PSO）等生物启发算法（BIA）对EEG和ECoG数据的有效性。支持向量机（SVM）分类器的性能表明，M-PSO在选择最少特征（SF）的情况下非常有效，并且在低迭代次数下能以可接受的速度收敛。
