论文标题
评估基于运动图像的BCI方法在帕金森氏病患者的神经康复中的评估
Evaluation of Motor Imagery-Based BCI methods in neurorehabilitation of Parkinson's Disease patients
论文作者
论文摘要
该研究报告了帕金森氏病（PD）患者的表现，以基于汽车塑料的脑部计算机界面（MI-BCI）进行了操作，并比较了三种选定的预处理和分类方法。该实验是针对7例PD患者进行的，这些PD患者总共进行了14个针对下肢的Mi-BCI课程。在每个会话的初始校准阶段记录了脑电图，并使用频谱加权的常见空间模式（SPECCSP），源功率综合（SPOC）和滤波器 - 银行通用空间模式（FBCSP）方法产生特定的BCI模型。结果表明，FBCSP在准确性方面优于SPOC，而SPOC和SPECCSP则在假阳性比率方面都超过了SPOC。该研究还表明，尽管精度较低，但PD患者能够操作MI-BCI。
The study reports the performance of Parkinson's disease (PD) patients to operate Motor-Imagery based Brain-Computer Interface (MI-BCI) and compares three selected pre-processing and classification approaches. The experiment was conducted on 7 PD patients who performed a total of 14 MI-BCI sessions targeting lower extremities. EEG was recorded during the initial calibration phase of each session, and the specific BCI models were produced by using Spectrally weighted Common Spatial Patterns (SpecCSP), Source Power Comodulation (SPoC) and Filter-Bank Common Spatial Patterns (FBCSP) methods. The results showed that FBCSP outperformed SPoC in terms of accuracy, and both SPoC and SpecCSP in terms of the false-positive ratio. The study also demonstrates that PD patients were capable of operating MI-BCI, although with lower accuracy.
