Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Comput Methods Programs Biomed. 2014 Mar;113(3):767-80. doi: 10.1016/j.cmpb.2013.12.020. Epub 2014 Jan 3.
Motor imagery (MI) tasks classification provides an important basis for designing brain-computer interface (BCI) systems. If the MI tasks are reliably distinguished through identifying typical patterns in electroencephalography (EEG) data, a motor disabled people could communicate with a device by composing sequences of these mental states. In our earlier study, we developed a cross-correlation based logistic regression (CC-LR) algorithm for the classification of MI tasks for BCI applications, but its performance was not satisfactory. This study develops a modified version of the CC-LR algorithm exploring a suitable feature set that can improve the performance. The modified CC-LR algorithm uses the C3 electrode channel (in the international 10-20 system) as a reference channel for the cross-correlation (CC) technique and applies three diverse feature sets separately, as the input to the logistic regression (LR) classifier. The present algorithm investigates which feature set is the best to characterize the distribution of MI tasks based EEG data. This study also provides an insight into how to select a reference channel for the CC technique with EEG signals considering the anatomical structure of the human brain. The proposed algorithm is compared with eight of the most recently reported well-known methods including the BCI III Winner algorithm. The findings of this study indicate that the modified CC-LR algorithm has potential to improve the identification performance of MI tasks in BCI systems. The results demonstrate that the proposed technique provides a classification improvement over the existing methods tested.
运动想象 (MI) 任务分类为脑机接口 (BCI) 系统的设计提供了重要依据。如果通过识别脑电图 (EEG) 数据中的典型模式可靠地区分 MI 任务,那么运动障碍者可以通过组合这些心理状态的序列与设备进行通信。在我们之前的研究中,我们开发了一种基于互相关的逻辑回归 (CC-LR) 算法,用于 BCI 应用中的 MI 任务分类,但它的性能并不令人满意。本研究开发了一种改进的 CC-LR 算法,探索了合适的特征集,以提高性能。改进的 CC-LR 算法使用国际 10-20 系统中的 C3 电极通道作为互相关 (CC) 技术的参考通道,并分别应用三个不同的特征集作为逻辑回归 (LR) 分类器的输入。目前的算法研究了哪个特征集最适合基于 EEG 数据描述 MI 任务的分布。本研究还深入探讨了如何考虑人脑的解剖结构,为 EEG 信号选择 CC 技术的参考通道。将所提出的算法与包括 BCI III 获胜算法在内的八种最近报道的知名方法进行了比较。本研究的结果表明,改进的 CC-LR 算法有潜力提高 BCI 系统中 MI 任务的识别性能。结果表明,与测试的现有方法相比,该技术提供了分类改进。