Android Feedback-Based Training Modulates Sensorimotor Rhythms During Motor Imagery
Android Feedback-Based Training Modulates Sensorimotor Rhythms During Motor Imagery
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DOI：
10.1109/tnsre.2018.2792481
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发表时间：
2018-03-01
影响因子：
4.9
通讯作者：
Nishio, Shuichi
中科院分区：
文献类型：
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作者：
Penaloza, Christian I.;Alimardani, Maryam;Nishio, Shuichi
基于脑电图的脑机接口（BCI）系统已被证明有潜力帮助患有破坏性运动瘫痪的患者。然而，人们对将BCI趋势转向针对健康用户的应用程序非常感兴趣。尽管脑机接口的操作取决于技术因素（即脑电模式分类算法）和人为因素（即人在多大程度上能够生成高质量的脑电模式），但对后者的研究最少。为了控制基于运动图像的脑机接口，用户需要学习通过使用带有抽象视觉反馈的经典训练方案练习运动图像来调节他们的感觉运动脑节奏。在本文中，我们研究了一种不同的脑机接口训练方案，使用类人机器人（Geminoid HI-2）来提供逼真的视觉反馈。所提出的训练协议解决了经典方法的不足，并利用了身体用户能力的优势。实验结果表明，基于机器人反馈的脑机接口训练改善了运动想象任务中感觉运动节律的调节。此外，我们还讨论了身体所有权转移错觉对机器人的影响可能对事件相关的非同步/同步活动的调节产生影响。
EEG-based brain computer interface (BCI) systems have demonstrated potential to assist patients with devastating motor paralysis conditions. However, there is great interest in shifting the BCI trend toward applications aimed at healthy users. Although BCI operation depends on technological factors (i.e., EEG pattern classification algorithm) and human factors (i.e., how well the person can generate good quality EEG patterns), it is the latter that is least investigated. In order to control a motor imagery-based BCI, users need to learn to modulate their sensorimotor brain rhythms by practicing motor imagery using a classical training protocol with an abstract visual feedback. In this paper, we investigate a different BCI training protocol using a human-like android robot (Geminoid HI-2) to provide realistic visual feedback. The proposed training protocol addresses deficiencies of the classical approach and takes the advantage of body-abled user capabilities. Experimental results suggest that android feedback-based BCI training improves the modulation of sensorimotor rhythms during motor imagery task. Moreover, we discuss how the influence of body ownership transfer illusion toward the android might have an effect on the modulation of event-related desyn-chronization/synchronization activity.