Nakagame Sho, Gorges Jeffrey, Nathan Kevin, Contreras-Vidal Jose L
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1548-1551. doi: 10.1109/EMBC.2016.7591006.
The feasibility of decoding lower limb kinematics in human treadmill walking from noninvasive electroencephalography (EEG) has been demonstrated with linear Wiener filter. However, nonlinear relationship between neural activities and limb movements may challenge the linear decoders in real-time brain computer interface (BCI) applications. In this study, we propose a nonlinear neural decoder using an Unscented Kalman Filter (UKF) to infer lower limb joint angles from noninvasive scalp EEG signals during human treadmill walking. Our results demonstrate that lower limb joint angles during treadmill walking can be decoded from the fluctuations in the amplitude of slow cortical potentials in the delta band (0.1-3Hz). Overall, the average decoding accuracy were 0.43 ± 0.18 for Pearson's r value and 1.82 ± 3.07 for signal to noise ratio (SNR), and robust to ocular, muscle, or movement artifacts. Moreover, the signal preprocessing scheme and the design of UKF allow the implementation of the proposed EEG-based BCI for real-time applications. This has implications for the development of closed-loop EEG-based BCI systems for gait rehabilitation after stroke.
利用线性维纳滤波器已证明从无创脑电图（EEG）解码人类在跑步机上行走时的下肢运动学是可行的。然而，神经活动与肢体运动之间的非线性关系可能会在实时脑机接口（BCI）应用中对线性解码器构成挑战。在本研究中，我们提出一种使用无迹卡尔曼滤波器（UKF）的非线性神经解码器，以在人类跑步机行走期间从无创头皮EEG信号推断下肢关节角度。我们的结果表明，跑步机行走期间的下肢关节角度可以从δ波段（0.1 - 3Hz）慢皮层电位幅度的波动中解码出来。总体而言，皮尔逊r值的平均解码准确率为0.43±0.18，信噪比（SNR）为1.82±3.07，并且对眼部、肌肉或运动伪影具有鲁棒性。此外，信号预处理方案和UKF的设计允许将所提出的基于EEG的BCI用于实时应用。这对开发用于中风后步态康复的基于EEG的闭环BCI系统具有启示意义。
