Ozdenizci Ozan, Gunay Sezen Yagmur, Quivira Fernando, Erdogmug Deniz
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1964-1967. doi: 10.1109/EMBC.2018.8512677.
We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.
我们提出了一种基于新颖分层图形模型的上下文感知混合脑机接口（hBMI），该模型使用脑电图（EEG）和肌电图（EMG）活动的概率融合。基于在静止执行期间收集的实验数据以及随后对双侧肢体的五种不同手势的成像，我们通过会话内和跨会话在线分类分析证明了所提出的hBMI系统的可行性。此外，我们通过模拟概率方法研究了该模型的上下文感知程度，并强调了我们的工作在神经生理学驱动的机器人手部假肢领域的潜在影响。
