Neuper Christa, Scherer Reinhold, Reiner Miriam, Pfurtscheller Gert
Ludwig Boltzmann-Institute for Medical Informatics and Neuroinformatics, Graz University of Technology, Graz, Austria.
Brain Res Cogn Brain Res. 2005 Dec;25(3):668-77. doi: 10.1016/j.cogbrainres.2005.08.014. Epub 2005 Oct 19.
Single-trial motor imagery classification is an integral part of a number of brain-computer interface (BCI) systems. The possible significance of the kind of imagery, involving rather kinesthetic or visual representations of actions, was addressed using the following experimental conditions: kinesthetic motor imagery (MIK), visual-motor imagery (MIV), motor execution (ME) and observation of movement (OOM). Based on multi-channel EEG recordings in 14 right-handed participants, we applied a learning classifier, the distinction sensitive learning vector quantization (DSLVQ) to identify relevant features (i.e., frequency bands, electrode sites) for recognition of the respective mental states. For ME and OOM, the overall classification accuracies were about 80%. The rates obtained for MIK (67%) were better than the results of MIV (56%). Moreover, the focus of activity during kinesthetic imagery was found close to the sensorimotor hand area, whereas visual-motor imagery did not reveal a clear spatial pattern. Consequently, to improve motor-imagery-based BCI control, user training should emphasize kinesthetic experiences instead of visual representations of actions.
单次试验运动想象分类是许多脑机接口(BCI)系统的一个组成部分。使用以下实验条件探讨了涉及动作的动觉或视觉表征的想象类型的可能意义:动觉运动想象(MIK)、视觉运动想象(MIV)、运动执行(ME)和动作观察(OOM)。基于14名右利手参与者的多通道脑电图记录,我们应用了一种学习分类器——区分敏感学习矢量量化(DSLVQ)来识别用于识别相应心理状态的相关特征(即频段、电极位置)。对于ME和OOM,总体分类准确率约为80%。MIK获得的准确率(67%)高于MIV的结果(56%)。此外,发现动觉想象期间的活动焦点靠近感觉运动手区,而视觉运动想象没有显示出明显的空间模式。因此,为了改善基于运动想象的BCI控制,用户训练应强调动觉体验而非动作的视觉表征。