Chai Rifai, Ling Sai Ho, Hunter Gregory P, Nguyen Hung T
Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, Broadway NSW 2007, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5266-9. doi: 10.1109/EMBC.2012.6347182.
This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels.
本文提出了一种基于非运动想象任务分类脑电图（EEG）的脑机接口（BCI），用于轮椅控制。它仅使用两个EEG通道和一个更好的特征提取器，以提高实际系统中的便携性和准确性。此外，比较了两种不同的特征提取方法，即功率谱密度（PSD）和希尔伯特-黄变换（HHT）能量，以使用基于遗传算法（GA）的神经网络分类器找到一种具有更高分类准确率的更好方法。来自五名受试者的结果表明，使用原始的八个通道进行三项任务时，准确率在76%至85%之间。仅使用两个通道并结合使用PSD特征提取器选择的最佳任务时，准确率降至65%至79%之间。然而，基于HHT的方法在使用两个EEG通道对三个有区别的任务进行分类时，准确率提高到了70%至84%之间。
