IEEE Trans Neural Syst Rehabil Eng. 2021;29:2008-2016. doi: 10.1109/TNSRE.2021.3115490. Epub 2021 Oct 1.
Decoding the motion intention of the human upper limb from electroencephalography (EEG) signals has important practical values. However, existing decoding models are built under the attended state while subjects perform motion tasks. In practice, people are often distracted by other tasks or environmental factors, which may impair decoding performance. To address this problem, in this paper, we propose a hierarchical decoding model of human upper limb motion intention from EEG signals based on attention state estimation. The proposed decoding model includes two components. First, the attention state detection (ASD) component estimates the attention state during the upper limb movement. Next, the motion intention recognition (MIR) component decodes the motion intention by using the decoding models built under the attended and distracted states. The experimental results show that the proposed hierarchical decoding model performs well under the attended and distracted states. This work can advance the application of human movement intention decoding and provides new insights into the study of brain-machine interfaces.
从脑电图 (EEG) 信号中解码人体上肢的运动意图具有重要的实际价值。然而，现有的解码模型是在被试进行运动任务时的注意状态下建立的。在实际中，人们经常会被其他任务或环境因素分心，这可能会降低解码性能。针对这个问题，本文提出了一种基于注意状态估计的人体上肢运动意图 EEG 信号的分层解码模型。所提出的解码模型包括两个组件。首先，注意状态检测 (ASD) 组件估计上肢运动时的注意状态。接下来，运动意图识别 (MIR) 组件通过使用在注意和分心状态下建立的解码模型来解码运动意图。实验结果表明，所提出的分层解码模型在注意和分心状态下表现良好。这项工作可以推动人体运动意图解码的应用，并为脑机接口的研究提供新的见解。
