UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.
J Neural Eng. 2019 Aug 6;16(5):056009. doi: 10.1088/1741-2552/ab3189.
Brain-computer interfaces (BCIs) are being developed to restore reach and grasping movements of paralyzed individuals. Recent studies have shown that the kinetics of grasping movement, such as grasp force, can be successfully decoded from electrocorticography (ECoG) signals, and that the high-frequency band (HFB) power changes provide discriminative information that contribute to an accurate decoding of grasp force profiles. However, as the models used in these studies contained simultaneous information from multiple spectral features over multiple areas in the brain, it remains unclear what parameters of movement and force are encoded by the HFB signals and how these are represented temporally and spatially in the SMC.
To investigate this, and to gain insight in the temporal dynamics of the HFB during grasping, we continuously modelled the ECoG HFB response recorded from nine individuals with epilepsy temporarily implanted with ECoG grids, who performed three different grasp force tasks.
We show that a model based on the force onset and offset consistently provides a better fit to the HFB power responses when compared with a model based on the force magnitude, irrespective of electrode location.
Our results suggest that HFB power, although potentially useful for continuous decoding, is more closely related to the changes in movement. This finding may potentially contribute to the more natural decoding of grasping movement in neural prosthetics.
脑机接口（BCI）的开发旨在恢复瘫痪个体的伸手和抓握运动能力。最近的研究表明，抓握运动的动力学，如抓握力，可以成功地从脑电（ECoG）信号中解码出来，并且高频带（HFB）功率变化提供了有区别的信息，有助于准确解码抓握力曲线。然而，由于这些研究中使用的模型包含来自大脑中多个区域的多个光谱特征的同时信息，因此尚不清楚 HFB 信号编码了哪些运动和力参数，以及这些参数在 SMC 中是如何在时间和空间上表示的。
为了研究这一点，并深入了解抓握过程中 HFB 的时间动态，我们连续对 9 名患有癫痫症的个体进行建模，这些个体暂时植入了 ECoG 网格，并执行了三种不同的抓握力任务。
我们发现，与基于力大小的模型相比，基于力起始和结束的模型始终能更好地拟合 HFB 功率响应，无论电极位置如何。
我们的结果表明，尽管 HFB 功率可能对连续解码有用，但它与运动的变化更为密切相关。这一发现可能有助于神经假体更自然地解码抓握运动。
