Wang Po T, King Christine E, McCrimmon Colin M, Lin Jack J, Sazgar Mona, Hsu Frank P K, Shaw Susan J, Millet David E, Chui Luis A, Liu Charles Y, Do An H, Nenadic Zoran
Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA.
J Neural Eng. 2016 Apr;13(2):026016. doi: 10.1088/1741-2560/13/2/026016. Epub 2016 Feb 9.
Electrocorticography (ECoG)-based brain-computer interface (BCI) is a promising platform for controlling arm prostheses. To restore functional independence, a BCI must be able to control arm prostheses along at least six degrees-of-freedoms (DOFs). Prior studies suggest that standard ECoG grids may be insufficient to decode multi-DOF arm movements. This study compared the ability of standard and high-density (HD) ECoG grids to decode the presence/absence of six elementary arm movements and the type of movement performed.
Three subjects implanted with standard grids (4 mm diameter, 10 mm spacing) and three with HD grids (2 mm diameter, 4 mm spacing) had ECoG signals recorded while performing the following movements: (1) pincer grasp/release, (2) wrist flexion/extension, (3) pronation/supination, (4) elbow flexion/extension, (5) shoulder internal/external rotation, and (6) shoulder forward flexion/extension. Data from the primary motor cortex were used to train a state decoder to detect the presence/absence of movement, and a six-class decoder to distinguish between these movements.
The average performances of the state decoders trained on HD ECoG data were superior (p = 3.05 × 10(-5)) to those of their standard grid counterparts across all combinations of the μ, β, low-γ, and high-γ frequency bands. The average best decoding error for HD grids was 2.6%, compared to 8.5% of standard grids (chance 50%). The movement decoders trained on HD ECoG data were superior (p = 3.05 × 10(-5)) to those based on standard ECoG across all band combinations. The average best decoding errors of 11.9% and 33.1% were obtained for HD and standard grids, respectively (chance error 83.3%). These improvements can be attributed to higher electrode density and signal quality of HD grids.
Commonly used ECoG grids are inadequate for multi-DOF BCI arm prostheses. The performance gains by HD grids may eventually lead to independence-restoring BCI arm prosthesis.
基于皮层脑电图（ECoG）的脑机接口（BCI）是控制手臂假肢的一个有前景的平台。为恢复功能独立性，一个BCI必须能够沿着至少六个自由度（DOF）来控制手臂假肢。先前的研究表明，标准的ECoG网格可能不足以解码多自由度的手臂运动。本研究比较了标准和高密度（HD）ECoG网格解码六种基本手臂运动的有无以及所执行运动类型的能力。
三名植入标准网格（直径4毫米，间距10毫米）和三名植入HD网格（直径2毫米，间距4毫米）的受试者在执行以下运动时记录ECoG信号：（1）钳形抓握/松开，（2）手腕屈伸，（3）旋前/旋后，（4）肘部屈伸，（5）肩部内/外旋转，以及（6）肩部前屈/后伸。来自初级运动皮层的数据用于训练一个状态解码器以检测运动的有无，以及一个六类解码器以区分这些运动。
在所有μ、β、低γ和高γ频段组合中，基于HD ECoG数据训练的状态解码器的平均性能优于（p = 3.05×10⁻⁵）其标准网格对应物。HD网格的平均最佳解码误差为2.6%，而标准网格为8.5%（机遇率为50%）。基于HD ECoG数据训练的运动解码器在所有频段组合中均优于（p = 3.05×10⁻⁵）基于标准ECoG的解码器。HD和标准网格的平均最佳解码误差分别为11.9%和33.1%（机遇误差为83.3%）。这些改进可归因于HD网格更高的电极密度和信号质量。
常用的ECoG网格不足以用于多自由度BCI手臂假肢。HD网格带来的性能提升最终可能促成恢复独立性的BCI手臂假肢。
