自动化学报Issue(6)：1045-1057,13.DOI:10.3724/SP.J.1004.2014.01045
基于运动相关皮层电位握力运动模式识别研究
Recognition of Actual Grip Force Movement Modes Based on Movement-related Cortical Potentials
摘要
Abstract
A new paradigm of grip force movement with parameters involving right and left hands is put forward in the study to meet the needs of brain-computer interface based brain-machine interaction control (BMIC) - direct brain-controlled robot interface (BCRI). Time-domain feature representation for grip force movement-related cortical potentials/movement-related potentials (MRPs) and the single-trial recognition of grip force movement modes are explored under the paradigm. EEG signals were picked up from eleven healthy sub jects during four different tasks of right and left hands. Subjects were asked to execute voluntary grip movement at two modes of grip force variation. Each task was executed 30 times in a random order repeatedly. The features having significant difference among different grip force tasks are used for the classification of grip force modes by Fisher linear discrimination analysis based on kernel function (k-FLDA) and support vector machine (SVM), respectively. The study further demonstrates that MRPs may reflect brain neural mechanism process for planning, execution and precision of a given grip movement task. The average misclassification rates of 24 ± 4% and 21 ± 5% across eleven subjects are achieved by k-FLDA and SVM, respectively. The minimum misclassification rate is 12% and the average of minimum misclassification rates across eleven sub jects is 20.9 ± 5%. The study is expected to lay a foundation for follow-up comparative researches, which provide some additional force control intention instructions for BMIC/BCRI.关键词
运动相关电位/握力运动模式/支持向量机/脑-机接口/脑-机交互控制/脑控机器人接口Key words
Movement-related potentials (MRPs)/grip force movement mode/support vector machine (SVM)/brain-computer interface (BCI)/brain-machine interaction control (BMIC)/brain-controlled robot interface (BCRI)引用本文复制引用
伏云发,徐保磊,李永程,李洪谊,王越超,余正涛..基于运动相关皮层电位握力运动模式识别研究[J].自动化学报,2014,(6):1045-1057,13.基金项目
国家自然科学基金青年基金(60705021),云南省应用基础研究计划项目(2013FB026),云南省级人培项目(KKSY201303048),云南省教育厅重点项目(2013Z130)资助@@@@Supported by National Natural Science Foundation of Youth Fund of China (60705021), Research Project for Application Foundation of Yunnan Province (2013FB026), Cultivation Pro-gram of Talents of Yunnan Province (KKSY201303048), and Fo-cal Program for Education Office of Yunnan Province (2013Z130) （60705021）
