Data from: Classifying three imaginary states of the same upper extremity using time-domain features

Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.

脑机接口（Brain-computer interface, BCI）可实现人类与机器的协同交互，其将大脑的电活动转化为可供机器识别的操控指令。本研究提出一种基于脑电图（electroencephalographic, EEG）信号的三类脑机接口准确率提升方法。该脑机接口用于区分静息状态与同一肢体的想象抓握、肘关节运动两类动作。由于同一肢体的想象运动在运动皮层区域具有相似的空间表征，该分类任务具有较高挑战性。所提方法提取时域特征，并采用搭载径向基核函数（radial basis kernel function, RBF）的支持向量机（support vector machine, SVM）完成分类。在本研究前期收集的、来自12名健康受试者的数据集上应用该方法时，获得了74.2%的平均分类准确率。该准确率高于共空间模式（common spatial patterns, CSP）、滤波共空间模式（filter bank CSP, FBCSP）及频段功率法等主流方法在同一数据集上的表现。上述结果令人振奋，所提方法有望在未来的脑机接口相关应用中得到推广，例如脑机接口驱动的机器人设备（如便携式上肢外骨骼），用以辅助上肢功能受损人群完成日常活动。