Electroencephalographic identifiers of motor adaptation learning
Electroencephalographic identifiers of motor adaptation learning
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DOI：
10.1088/1741-2552/aa6abd
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发表时间：
2017-08-01
影响因子：
4
通讯作者：
Cetin, Mujdat
中科院分区：
文献类型：
--
作者：
Ozdenizci, Ozan;Yalcin, Mustafa;Cetin, Mujdat
目标。最近的脑机接口（BCI）辅助中风康复方案倾向于关注大脑的感觉运动活动。有证据表明，感觉运动区以外的各种脑节律与运动缺陷的程度有关，我们建议在空间和频谱上识别感觉运动区以外的运动学习的神经相关因素，以进一步用于新型脑机接口辅助的神经康复设置。的方法。记录了健康受试者通过机器人手柄进行体力场适应任务时的脑电图数据。实验前休息和试验前运动准备期间记录的脑电图活动作为预测受试者运动适应学习表现的特征。主要的结果。实验对象学习在力场下以不同的适应速度做直线运动。静息状态和试验前脑电图特征都与广泛的β活动网络相关，可以预测个体的适应率。除感觉运动区外，被试的顶叶-枕叶皮质成分在预测中起着重要作用，额顶叶皮质成分显示，适应率高的被试者的试前β能力显著下降，适应率低的被试者的试前β能力显著增加。的意义。包括感觉运动区域在内，一个大规模的β活动网络被认为是运动学习的预测。静息状态顶叶-枕叶β活动或试验前额-顶叶β活动的强度可以在脑机接口辅助中风康复方案中考虑，通过神经反馈训练或意志控制脑-机器人界面的神经活动来诱导可塑性。
Objective. Recent brain-computer interface (BCI) assisted stroke rehabilitation protocols tend to focus on sensorimotor activity of the brain. Relying on evidence claiming that a variety of brain rhythms beyond sensorimotor areas are related to the extent of motor deficits, we propose to identify neural correlates of motor learning beyond sensorimotor areas spatially and spectrally for further use in novel BCI-assisted neurorehabilitation settings. Approach. Electroencephalographic (EEG) data were recorded from healthy subjects participating in a physical force-field adaptation task involving reaching movements through a robotic handle. EEG activity recorded during rest prior to the experiment and during pre-trial movement preparation was used as features to predict motor adaptation learning performance across subjects. Main results. Subjects learned to perform straight movements under the force-field at different adaptation rates. Both resting-state and pre-trial EEG features were predictive of individual adaptation rates with relevance of a broad network of beta activity. Beyond sensorimotor regions, a parieto-occipital cortical component observed across subjects was involved strongly in predictions and a fronto-parietal cortical component showed significant decrease in pre-trial beta-powers for users with higher adaptation rates and increase in pre-trial beta-powers for users with lower adaptation rates. Significance. Including sensorimotor areas, a large-scale network of beta activity is presented as predictive of motor learning. Strength of resting-state parieto-occipital beta activity or pre-trial fronto-parietal beta activity can be considered in BCI-assisted stroke rehabilitation protocols with neurofeedback training or volitional control of neural activity for brain-robot interfaces to induce plasticity.