The power of multivariate approach in identifying EEG correlates of interlimb coupling.
The power of multivariate approach in identifying EEG correlates of interlimb coupling.
复制标题
DOI：
10.3389/fnhum.2023.1256497
复制
发表时间：
2023
影响因子：
2.9
通讯作者：
Ossmy, Ori
中科院分区：
文献类型：
--
作者：
Hascher, Sophie;Shuster, Anastasia;Mukamel, Roy;Ossmy, Ori
肢体间耦合是指一个肢体的运动与其他肢体的运动之间的相互作用。理解这种效应背后的机制对真实的生活很重要，因为它反映了肢体之间的相互依赖程度，在日常活动中发挥作用，包括使用工具，烹饪或演奏乐器。肢体间耦合涉及多个大脑区域共同工作，包括协调两个半球的感觉和运动区域的神经活动。传统的神经科学研究采用单变量方法来识别与行为耦合措施相对应的神经特征。然而，这种方法将肢体间任务期间神经活动的复杂性降低到一个值。在这份简短的研究报告中，我们认为识别肢体间耦合的神经相关因素将受益于多元方法，其中使用来自多个来源的完整模式来预测行为耦合。我们证明了这种方法的可行性，在探索性脑电图研究中，参与者（n = 10）完成了240个试验的一个完善的绘图范式，涉及interlimb耦合。使用人工神经网络（ANN），我们表明，多变量表示的EEG信号显着捕获interlimb耦合在双手绘图，而单变量分析未能识别这种相关性。我们的研究结果表明，分析多个EEG通道的分布模式比单值技术更敏感，可以发现多个神经信号之间的细微差异。使用这种技术可以改善复杂运动行为的神经相关性的识别。
Interlimb coupling refers to the interaction between movements of one limb and movements of other limbs. Understanding mechanisms underlying this effect is important to real life because it reflects the level of interdependence between the limbs that plays a role in daily activities including tool use, cooking, or playing musical instruments. Interlimb coupling involves multiple brain regions working together, including coordination of neural activity in sensory and motor regions across the two hemispheres. Traditional neuroscience research took a univariate approach to identify neural features that correspond to behavioural coupling measures. Yet, this approach reduces the complexity of the neural activity during interlimb tasks to one value. In this brief research report, we argue that identifying neural correlates of interlimb coupling would benefit from a multivariate approach in which full patterns from multiple sources are used to predict behavioural coupling. We demonstrate the feasibility of this approach in an exploratory EEG study where participants (n = 10) completed 240 trials of a well-established drawing paradigm that involves interlimb coupling. Using artificial neural network (ANN), we show that multivariate representation of the EEG signal significantly captures the interlimb coupling during bimanual drawing whereas univariate analyses failed to identify such correlates. Our findings demonstrate that analysing distributed patterns of multiple EEG channels is more sensitive than single-value techniques in uncovering subtle differences between multiple neural signals. Using such techniques can improve identification of neural correlates of complex motor behaviours.
登录
查看更多内容
影响因子：
3.7
作者：
Groppe DM;Urbach TP;Kutas M
通讯作者：
Kutas M
影响因子：
5.7
作者：
Debaere, F;Swinnen, SP;Duysens, J
通讯作者：
Duysens, J
影响因子：
3.3
作者：
Desrochers, Phillip C.;Brunfeldt, Alexander T.;Kagerer, Florian A.
通讯作者：
Kagerer, Florian A.
通过深度学习实现开放世界脑电图解码：一项综合调查
影响因子：
14.9
作者：
Chen, Xun;Li, Chang;Wang, Z. Jane
通讯作者：
Wang, Z. Jane
影响因子：
2.9
作者：
Garbarini F;Turella L;Rabuffetti M;Cantagallo A;Piedimonte A;Fainardi E;Berti A;Fadiga L
通讯作者：
Fadiga L