Mondini Valeria, Mangia Anna Lisa, Cappello Angelo
Department of Electrical, Electronic and Information Engineering (DEI), University of Bologna, Viale del Risorgimento, 40136 Bologna, Italy.
Comput Intell Neurosci. 2016;2016:4562601. doi: 10.1155/2016/4562601. Epub 2016 Aug 17.
Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously adapting the underlying classifier model. The present work describes a cue-paced, EEG-based BCI system using motor imagery that falls within the category of the previously mentioned ones. Specifically, our adaptive strategy includes a simple scheme based on a common spatial pattern (CSP) method and support vector machine (SVM) classification. The system's efficacy was proved by online testing on 10 healthy participants. In addition, we suggest some features we implemented to improve a system's "flexibility" and "customizability," namely, (i) a flexible training session, (ii) an unbalancing in the training conditions, and (iii) the use of adaptive thresholds when giving feedback.
运动想象是基于脑电图的脑机接口（BCI）中一种常见的控制策略。然而，通过想象运动来对感觉运动节律（SMR）进行自主控制可能既需要技巧又不直观，并且通常需要用户进行不同程度的训练。为了加快训练进程，人们提出了一整类BCI系统，这类系统在持续调整底层分类器模型的同时，尽可能早地提供反馈。本研究描述了一种基于脑电图、采用运动想象的提示节奏型BCI系统，它属于上述BCI系统类别。具体而言，我们的自适应策略包括一种基于共空间模式（CSP）方法和支持向量机（SVM）分类的简单方案。该系统的有效性通过对10名健康参与者进行在线测试得到了验证。此外，我们介绍了为提高系统“灵活性”和“可定制性”而实施的一些特性：（i）灵活的训练环节；（ii）训练条件的不平衡设置；（iii）反馈时采用自适应阈值。
