Zhang Jing, Shen Cheng, Chen Weihai, Ma Xinzhi, Liang Zilin, Zhang Yue
School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191 China.
School of Artificial Intelligence, Shenyang Aerospace University, Shenyang, 110136 Liaoning Province China.
Cogn Neurodyn. 2024 Dec;18(6):3859-3872. doi: 10.1007/s11571-024-10164-3. Epub 2024 Sep 1.
The decoding of electroencephalogram (EEG) signals, especially motion-related cortical potentials (MRCP), is vital for the early detection of motor intent before movement execution. To enhance the decoding accuracy of MRCP and promote the application of early motion intention in active rehabilitation training, we propose a method for decoding MRCP signals. Specifically, an experimental paradigm is designed for the efficient capture of MRCP signals. Moreover, a feature extraction method based on differentiation is proposed to effectively characterize action variability. Six subjects were recruited to validate the effectiveness of the decoding method. Experiments such as fixed-window classification, sliding-window detection, and asynchronous analysis demonstrate that the method can detect motion intention 316 milliseconds before action execution and is capable of continuously detecting both rapid and slow movements.
脑电图（EEG）信号的解码，尤其是与运动相关的皮层电位（MRCP），对于在运动执行前早期检测运动意图至关重要。为了提高MRCP的解码准确性并促进早期运动意图在主动康复训练中的应用，我们提出了一种解码MRCP信号的方法。具体而言，设计了一种实验范式以有效捕获MRCP信号。此外，提出了一种基于微分的特征提取方法以有效表征动作变异性。招募了6名受试者来验证解码方法的有效性。固定窗口分类、滑动窗口检测和异步分析等实验表明，该方法能够在动作执行前316毫秒检测到运动意图，并且能够连续检测快速和慢速运动。
