论文标题
预循环：基于EEG前的手动运动学估算
PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp and Lift task
论文作者
论文摘要
大脑活动解码的运动学有助于开发康复或发电的脑部计算机界面设备。从非侵入性脑电图（EEG）记录的低频信号与用于运动轨迹解码（MTD）的神经运动相关性有关。在这种交流中，研究了健康参与者的EEG解码运动运动学轨迹（0.5-3 Hz）的能力。特别是，提出了两个基于学习的神经解码器，称为premovnet-i和prepovnet-ii，它们使用前体内脑电图数据中存在的与运动相关的神经信息。在运动开始前，EEG数据段的不同时间延迟为150 ms，200 ms，250 ms，300 ms和350毫秒。 MTD用于使用脑电图的GRASP-LIFT任务（Way-EEG-GAL数据集），并使用EEG，并将各种滞后作为神经解码器输入。将提出的解码器的性能与最新的多变量线性回归（MLR）模型进行了比较。 Pearson相关系数和手轨迹被用作性能度量。结果表明，使用前移动性脑电图数据解码3D手运动学的生存能力，从而可以更好地控制基于BCI的外部设备，例如外骨骼/外部设备。
Kinematics decoding from brain activity helps in developing rehabilitation or power-augmenting brain-computer interface devices. Low-frequency signals recorded from non-invasive electroencephalography (EEG) are associated with the neural motor correlation utilised for motor trajectory decoding (MTD). In this communication, the ability to decode motor kinematics trajectory from pre-movement delta-band (0.5-3 Hz) EEG is investigated for the healthy participants. In particular, two deep learning-based neural decoders called PreMovNet-I and PreMovNet-II, are proposed that make use of motor-related neural information existing in the pre-movement EEG data. EEG data segments with various time lags of 150 ms, 200 ms, 250 ms, 300 ms, and 350 ms before the movement onset are utilised for the same. The MTD is presented for grasp-and-lift task (WAY-EEG-GAL dataset) using EEG with the various lags taken as input to the neural decoders. The performance of the proposed decoders are compared with the state-of-the-art multi-variable linear regression (mLR) model. Pearson correlation coefficient and hand trajectory are utilised as performance metric. The results demonstrate the viability of decoding 3D hand kinematics using pre-movement EEG data, enabling better control of BCI-based external devices such as exoskeleton/exosuit.
