Objective. 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. Approach. 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. Main results. 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%). Significance. 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.

客观的。脑机接口 (BCI) 系统可用于为患有肌萎缩侧索硬化症 (ALS) 等神经肌肉疾病的患者提供运动和交流帮助。在运动执行过程中自然产生的运动相关皮层电位 (MRCP) 可用于实现由运动尝试触发的 BCI。这种 BCI 可以帮助 ALS 患者在疾病进展期间受损的运动功能，并促进生成可靠 MRCP 的训练。培训方面与在疾病后期建立沟通渠道有关。因此，本研究的目的是探讨在 ALS 患者中检测与运动意图相关的 MRCPs 的可能性，这些患者的疾病进展程度从轻微到完全瘫痪。方法。在 30 名处于疾病不同阶段的 ALS 患者执行或尝试执行与视觉提示同步的手部运动时，从 9 个通道记录了脑电图信号。运动检测是使用运动和静止阶段之间的离线分类来实现的。使用具有 50% 重叠的 500 ms 滑动窗口提取时间和光谱特征。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，对每个单独通道和两个代理通道进行检测。主要结果。结果表明，所有患者的检测性能都很高（准确率 80.5 5.6%），但导致最佳性能的分类参数（通道、特征和分类器）因患者而异。当对所有患者使用相同的通道和分类器时（参与者通用分析），性能显着下降（准确率 74 8.3%）。意义。本研究表明，为了在疾病的不同阶段最大限度地检测 ALS 患者的脑电波，应该针对每个患者单独调整分类管道。