Kinematic parameter (KP) estimation from early electroencephalogram (EEG) signals is essential for positive augmentation using wearable robot. However, work related to early estimation of KPs from surface EEG is sparse. In this work, a deep learning-based model, BiCurNet, is presented for early estimation of biceps curl using collected EEG signal. The model utilizes light-weight architecture with depth-wise separable convolution layers and customized attention module. The feasibility of early estimation of KPs is demonstrated using brain source imaging. Computationally efficient EEG features in spherical and head harmonics domain is utilized for the first time for KP prediction. The best Pearson correlation coefficient (PCC) between estimated and actual trajectory of $0.7$ is achieved when combined EEG features (spatial and harmonics domain) in delta band is utilized. Robustness of the proposed network is demonstrated for subject-dependent and subject-independent training, using EEG signals with artifacts.

翻译：从早期脑电图信号中估计运动学参数对于利用可穿戴机器人实现正向增强至关重要。然而，基于头皮脑电信号进行早期运动学参数估计的相关研究较为匮乏。本文提出了一种基于深度学习的模型BiCurNet，利用采集的脑电信号实现对肱二头肌弯举动作的早期估计。该模型采用轻量化架构，结合深度可分离卷积层与定制化注意力模块。通过脑源成像技术验证了早期运动学参数估计的可行性。首次将球面域和头谐波域中计算高效的脑电特征用于运动学参数预测。当使用δ频段脑电特征（空间域与谐波域联合）时，估计轨迹与实际轨迹的最佳皮尔逊相关系数达到0.7。实验证明，该网络在包含伪迹的脑电信号上对受试者依赖与受试者独立训练均具有鲁棒性。