Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Science and Research Centre, University of Belgrade-School of Electrical Engineering, Belgrade 11000, Serbia.
J Neural Eng. 2021 Sep 6;18(5). doi: 10.1088/1741-2552/ac15e3.
Brain-computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis.Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis.The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%).The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.
脑机接口（BCI）系统可用于为患有神经肌肉疾病（如肌萎缩性侧索硬化症（ALS））的患者提供运动和通信辅助。运动相关皮质电位（MRCPs）在运动执行过程中自然产生，可用于实现由运动尝试触发的 BCI。这种 BCI 可以在疾病进展过程中辅助 ALS 患者受损的运动功能，并促进产生可靠的 MRCPs 的训练。训练方面与在疾病后期建立通信通道有关。因此，本研究的目的是研究在从轻度瘫痪到完全瘫痪的不同疾病进展阶段的 ALS 患者中检测与运动意图相关的 MRCPs 的可能性。
在疾病的不同阶段，从 9 个通道记录了 30 名 ALS 患者的脑电图信号，同时他们按照视觉提示定时执行或尝试执行手部运动。使用离线分类在运动和休息阶段之间进行运动检测。使用具有 50%重叠的 500ms 滑动窗口提取时间和频谱特征。通过执行特征选择，然后使用线性和非线性支持向量机和线性判别分析进行分类，针对每个单独的通道和两个替代通道测试检测。
结果表明，所有患者的检测性能均很高（准确率为 80.5±5.6%），但导致最佳性能的分类参数（通道、特征和分类器）在患者之间差异很大。当相同的通道和分类器用于所有患者（参与者通用分析）时，性能显着下降（准确率为 74±8.3%）。
本研究表明，为了最大限度地提高不同疾病阶段的 ALS 患者脑波的检测，分类管道应针对每个患者进行单独调整。
