Department of Electronics, Instrumentation & Control Engineering, School of Electrical & Electronics Engineering, Fiji National University, Samabula, Fiji.
School of Engineering and Physics, Faculty of Science, Technology & Environment, The University of the South Pacific, Suva, Fiji.
Med Biol Eng Comput. 2018 Oct;56(10):1861-1874. doi: 10.1007/s11517-018-1821-4. Epub 2018 Apr 4.
A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals. Graphical abstract ᅟ.
脑机接口 (BCI) 系统允许大脑和外部世界之间进行直接通信。常见空间模式 (CSP) 已被有效地用于 BCI 系统中数据的特征提取。然而，许多研究表明，使用 CSP 的 BCI 系统的性能在很大程度上取决于滤波器参数。产生最具辨别力信息的滤波器参数因个体而异，手动调整滤波器参数是一项困难且耗时的工作。在本文中，我们提出了一种用于运动想象脑电图 (EEG) 信号分类的新的自动化滤波器调整方法，该方法可以自动灵活地找到最佳性能的滤波器参数。我们在两个公共基准数据集上评估了我们提出的方法的性能。与现有的传统 CSP 方法相比，我们的方法分别将 BCI 竞赛 III 数据集 IVa 和 BCI 竞赛 IV 数据集 I 的平均分类错误率降低了 2.89%和 3.61%。此外，与本文研究的最先进方法相比，我们提出的方法还实现了最低的平均分类错误率。因此，我们提出的方法可用于开发改进的 BCI 系统，这些系统可以帮助残疾人士恢复对环境的控制。它还可以用于增强疾病识别，例如使用 EEG 信号检测癫痫发作。
