School of Information Engineering, Wuhan University of Technology, Wuhan 430070, People's Republic of China.
J Neural Eng. 2019 Apr;16(2):026032. doi: 10.1088/1741-2552/ab0328. Epub 2019 Jan 30.
A motor-imagery-based brain-computer interface (MI-BCI) provides an alternative way for people to interface with the outside world. However, the classification accuracy of MI signals remains challenging, especially with an increased number of classes and the presence of high variations with data from multiple individual people. This work investigates electroencephalogram (EEG) signal processing techniques, aiming to enhance the classification performance of multiple MI tasks in terms of tackling the challenges caused by the vast variety of subjects.
This work introduces a novel method to extract discriminative features by combining the features of functional brain networks with two other feature extraction algorithms: common spatial pattern (CSP) and local characteristic-scale decomposition (LCD). After functional brain networks are established from the MI EEG signals of the subjects, the measures of degree in the binary networks are extracted as additional features and fused with features in the frequency and spatial domains extracted by the CSP and LCD algorithms. A real-time BCI robot control system is designed and implemented with the proposed method. Subjects can control the movement of the robot through four classes of MI tasks. Both the BCI competition IV dataset 2a and real-time data acquired in our designed system are used to validate the performance of the proposed method.
As for the offline data experiment results, the average classification accuracy of the proposed method reaches 79.7%, outperforming the majority of popular algorithms. Experimental results with real-time data also prove the proposed method to be highly promising in its real-time performance.
The experimental results show that our proposed method is robust in extracting discriminative brain activity features when performing different MI tasks, hence improving the classification accuracy in four-class MI tasks. The high classification accuracy and low computational demand show a considerable practicality for real-time rehabilitation systems.
基于运动想象的脑机接口 (MI-BCI) 为人们与外界进行交互提供了一种替代方式。然而，MI 信号的分类准确性仍然具有挑战性，尤其是在类别数量增加且来自多个个体的数据存在高度变化的情况下。本研究旨在调查脑电图 (EEG) 信号处理技术，旨在通过处理由大量不同主体引起的挑战，提高多 MI 任务的分类性能。
本研究引入了一种新方法，通过将功能脑网络的特征与另外两种特征提取算法（共空间模式 (CSP) 和局部特征尺度分解 (LCD)）相结合，提取判别特征。在从受试者的 MI EEG 信号建立功能脑网络之后，提取二进制网络中的度度量作为附加特征，并与 CSP 和 LCD 算法提取的频率和空间域特征融合。设计并实现了具有所提出方法的实时 BCI 机器人控制系统。受试者可以通过四个 MI 任务控制机器人的运动。使用所提出的方法验证了 BCI 竞赛 IV 数据集 2a 和我们设计的系统中实时采集的数据的性能。
对于离线数据实验结果，所提出方法的平均分类准确率达到 79.7%，优于大多数流行算法。实时数据的实验结果也证明了所提出的方法在实时性能方面具有很高的应用前景。
实验结果表明，我们提出的方法在执行不同 MI 任务时能够稳健地提取判别脑活动特征，从而提高了四分类 MI 任务的分类准确性。高分类准确性和低计算需求表明，该方法在实时康复系统中具有相当的实用性。
