Liang Shuang, Choi Kup-Sze, Qin Jing, Wang Qiong, Pang Wai-Man, Heng Pheng-Ann
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China.
School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
Technol Health Care. 2016 Apr 29;24 Suppl 2:S795-801. doi: 10.3233/THC-161212.
The effective connectivity refers explicitly to the influence that one neural system exerts over another in frequency domain. To investigate the propagation of neuronal activity in certain frequency can help us reveal the mechanisms of information processing by brain.
This study investigates the detection of effective connectivity and analyzes the complex brain network connection mode associated with motor imagery (MI) tasks.
The effective connectivity among the primary motor area is firstly explored using partial directed coherence (PDC) combined with multivariate empirical mode decomposition (MEMD) based on electroencephalography (EEG) data. Then a new approach is proposed to analyze the connection mode of the complex brain network via the information flow pattern.
Our results demonstrate that significant effective connectivity exists in the bilateral hemisphere during the tasks, regardless of the left-/right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of the contralateral lateralization. The classification performance of left-/right-hand MI tasks can be improved by careful selection of intrinsic mode functions (IMFs).
The proposed method can provide efficient features for the detection of MI tasks and has great potential to be applied in brain computer interface (BCI).
有效连接明确指的是一个神经系统在频域中对另一个神经系统施加的影响。研究特定频率下神经元活动的传播有助于我们揭示大脑信息处理的机制。
本研究调查有效连接的检测，并分析与运动想象（MI）任务相关的复杂脑网络连接模式。
首先基于脑电图（EEG）数据，使用偏相干（PDC）结合多变量经验模态分解（MEMD）探索初级运动区之间的有效连接。然后提出一种新方法，通过信息流模式分析复杂脑网络的连接模式。
我们的结果表明，在任务期间双侧半球存在显著的有效连接，无论左手/右手MI任务如何。此外，信息流的出入率结果揭示了对侧偏侧化的存在。通过仔细选择本征模函数（IMF）可以提高左手/右手MI任务的分类性能。
所提出的方法可为MI任务的检测提供有效特征，在脑机接口（BCI）中具有很大的应用潜力。
