IEEE Trans Neural Syst Rehabil Eng. 2020 Mar;28(3):687-698. doi: 10.1109/TNSRE.2020.2966826. Epub 2020 Jan 15.
An important challenge in developing a movement-related cortical potential (MRCP)-based brain-machine interface (BMI) is an accurate decoding of the user intention for real-world environments. However, the performance remains insufficient for real-time decoding owing to the endogenous signal characteristics compared to other BMI paradigms. This study aims to enhance the MRCP decoding performance from the perspective of preprocessing techniques (i.e., spectral filtering). To the best of our knowledge, existing MRCP studies have used spectral filters with a fixed frequency bandwidth for all subjects. Hence, we propose a subject-dependent and section-wise spectral filtering (SSSF) method that considers the subjects' individual MRCP characteristics for two different temporal sections. In this study, MRCP data were acquired under a powered exoskeleton environments in which the subjects conducted self-initiated walking. We evaluated our method using both our experimental data and a public dataset (BNCI Horizon 2020). The decoding performance using the SSSF was 0.86 (±0.09), and the performance on the public dataset was 0.73 (±0.06) across all subjects. The experimental results showed a statistically significant enhancement ( ) compared with the fixed frequency bands used in previous methods on both datasets. In addition, we presented successful decoding results from a pseudo-online analysis. Therefore, we demonstrated that the proposed SSSF method can involve more meaningful MRCP information than conventional methods.
开发基于运动相关皮层电位 (MRCP) 的脑机接口 (BMI) 的一个重要挑战是准确解码用户在真实环境中的意图。然而，与其他 BMI 范式相比，由于内源性信号特征，其性能仍然不足以实时解码。本研究旨在从预处理技术（即光谱滤波）的角度提高 MRCP 解码性能。据我们所知，现有的 MRCP 研究已经为所有受试者使用了具有固定频率带宽的光谱滤波器。因此，我们提出了一种基于受试者和节段的频谱滤波 (SSSF) 方法，该方法考虑了受试者在两个不同时间部分的个体 MRCP 特征。在这项研究中，MRCP 数据是在受试者进行自主发起行走的动力外骨骼环境中采集的。我们使用我们的实验数据和公共数据集（BNCI Horizon 2020）评估了我们的方法。使用 SSSF 的解码性能为 0.86（±0.09），在所有受试者中，在公共数据集上的性能为 0.73（±0.06）。实验结果表明，与以前方法中使用的固定频带相比，在两个数据集上均显示出统计学上的显著增强（ ）。此外，我们还展示了伪在线分析的成功解码结果。因此，我们证明了所提出的 SSSF 方法可以比传统方法包含更有意义的 MRCP 信息。
