Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:6029-6032. doi: 10.1109/EMBC46164.2021.9630384.
EEG-EMG based hybrid Brain Computer Interface (hBCI) utilizes the brain-muscle physiological system to interpret and identify motor behaviors, and transmit human intelligence to automated machines in AI applications such as neurorehabilitations and brain-like intelligence. The study introduces a hBCI method for motor behaviors, where multiple time series of the brain neuromuscular network are introduced to indicate brain-muscle causal interactions, and features are extracted based on Relative Causal Strengths (RCSs) derived by Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) based Causal Decomposition. The complex process in brain neuromuscular interactions is specifically investigated towards a monitoring task of upper limb movement, whose 63-channel EEGs and 2-channel EMGs are composed of data inputs. The energy and frequency factors counted from RCSs were extracted as Core Features (CFs). Results showed accuracies of 91.4% and 81.4% with CFs for identifying cascaded (No Movement and Movement Execution) and 3-class (No Movement, Right Movement, and Left Movement) using Naive Bayes classifier, respectively. Moreover, those reached 100% and 94.3% when employing CFs combined with eigenvalues processed by Common Spatial Pattern (CSP). This initial work implies a novel causality inference based hBCI solution for the detection of human upper limb movement.
基于脑电图-肌电图的混合脑机接口（hBCI）利用大脑-肌肉生理系统来解释和识别运动行为，并在人工智能应用中，如神经康复和类脑智能中，将人类智能传输到自动化机器。本研究介绍了一种用于运动行为的 hBCI 方法，其中引入了多个脑肌网络的时间序列来指示脑-肌肉因果相互作用，并基于噪声辅助多变量经验模态分解（NA-MEMD）基于因果分解提取的相对因果强度（RCS）提取特征。具体研究了脑肌相互作用中的复杂过程，以进行上肢运动的监测任务，其 63 通道脑电图和 2 通道肌电图由数据输入组成。从 RCS 中计算出的能量和频率因素被提取为核心特征（CFs）。结果表明，使用朴素贝叶斯分类器分别识别级联（无运动和运动执行）和 3 类（无运动、右运动和左运动）时，CFs 的准确率分别为 91.4%和 81.4%。此外，当使用 CFs 结合共空间模式（CSP）处理的特征值时，准确率分别达到 100%和 94.3%。这项初步工作意味着一种基于因果推理的新型 hBCI 解决方案，用于检测人类上肢运动。
