Sburlea Andreea I, Wilding Marilena, Müller-Putz Gernot R
Institute of Neural Engineering, Graz University of Technology, Graz, 8010, Stremayrgasse 16/4, Styria, Austria.
BioTechMed Graz, Austria.
Neuroimage Rep. 2021 Jun 1;1(2):100012. doi: 10.1016/j.ynirp.2021.100012. eCollection 2021 Jun.
Grasping movements are known to activate the fronto-parietal brain networks both in human and non-human primates. However, it is unclear if these activations represent properties of the objects or hand postures or both at different stages of the movement. We manipulated the intrinsic properties of the objects and the grasping types in order to create twelve unique combinations of grasping movements and we investigated, in healthy adult humans, the low-frequency time-domain EEG representation of grasping over different stages of the movement. Next, we implemented two multiclass decoders for the grasp type and objects' properties and evaluated them over time. Furthermore, we investigated the similarity between these grasping EEG representations and categorical models that encode properties of the movement and intrinsic properties of the objects. We found that properties of the grasping movement (grasp types, number of fingers) and intrinsic object properties (shape and size) as represented in EEG are encoded in different brain areas throughout the movement stages. Both object properties and grasp types can be decoded significantly above chance level using low-frequency EEG activity during the planning and execution of the movement. Moreover, we found that this preferential time-wise encoding allows the decoding of object properties already from the observation stage, while the grasp type can also be accurately decoded at the object release stage. These findings contribute to the understanding of the grasping representation based on noninvasive EEG brain signals, and its evolution over the course of movement in relation to categorical models that describe the grasped object's properties or that encode properties of the grasping movement. Moreover, our multiclass grasping decoders are informative for the design and implementation of noninvasive motor control strategies.
众所周知，抓握动作会激活人类和非人类灵长类动物的额顶脑网络。然而，目前尚不清楚这些激活是代表物体的属性、手部姿势，还是运动不同阶段两者的属性。我们操纵了物体的固有属性和抓握类型，以创建十二种独特的抓握动作组合，并在健康成年人中研究了抓握动作在不同运动阶段的低频时域脑电图表现。接下来，我们针对抓握类型和物体属性实现了两个多类解码器，并随时间对它们进行评估。此外，我们研究了这些抓握脑电图表现与对运动属性和物体固有属性进行编码的分类模型之间的相似性。我们发现，脑电图中所代表的抓握动作属性（抓握类型、手指数量）和物体固有属性（形状和大小）在整个运动阶段是在不同脑区进行编码的。在运动的计划和执行过程中，使用低频脑电图活动，物体属性和抓握类型都可以显著高于机会水平进行解码。此外，我们发现这种按时间优先编码允许在观察阶段就对物体属性进行解码，而抓握类型在物体释放阶段也可以被准确解码。这些发现有助于基于非侵入性脑电图脑信号理解抓握表现，以及其在运动过程中相对于描述被抓握物体属性或对抓握动作属性进行编码的分类模型的演变。此外，我们的多类抓握解码器对于非侵入性运动控制策略的设计和实施具有参考价值。
