Objective. Brain–computer interfaces (BCIs) show promise as a direct line of communication between the brain and the outside world that could benefit those with impaired motor function. But the commands available for BCI operation are often limited by the ability of the decoder to differentiate between the many distinct motor or cognitive tasks that can be visualized or attempted. Simple binary command signals (e.g. right hand at rest versus movement) are therefore used due to their ability to produce large observable differences in neural recordings. At the same time, frequent command switching can impose greater demands on the subject’s focus and takes time to learn. Here, we attempt to decode the degree of effort in a specific movement task to produce a graded and more flexible command signal. Approach. Fourteen healthy human subjects (nine male, five female) responded to visual cues by squeezing a hand dynamometer to different levels of predetermined force, guided by continuous visual feedback, while the electroencephalogram (EEG) and grip force were monitored. Movement-related EEG features were extracted and modeled to predict exerted force. Main results. We found that event-related desynchronization (ERD) of the 8–30 Hz mu-beta sensorimotor rhythm of the EEG is separable for different degrees of motor effort. Upon four-fold cross-validation, linear classifiers were found to predict grip force from an ERD vector with mean accuracies across subjects of 53% and 55% for the dominant and non-dominant hand, respectively. ERD amplitude increased with target force but appeared to pass through a trough that hinted at non-monotonic behavior. Significance. Our results suggest that modeling and interactive feedback based on the intended level of motor effort is feasible. The observed ERD trends suggest that different mechanisms may govern intermediate versus low and high degrees of motor effort. This may have utility in rehabilitative protocols for motor impairments.

目标。脑机接口 (BCI) 作为大脑与外部世界之间的直接通信线路显示出前景，可以使运动功能受损的人受益。但是，可用于 BCI 操作的命令通常受到解码器区分可以可视化或尝试的许多不同运动或认知任务的能力的限制。因此使用简单的二进制命令信号（例如右手与运动），因为它们能够在神经记录中产生大的可观察差异。同时，频繁的指令切换会对主体的注意力提出更高的要求，需要时间来学习。在这里，我们尝试解码特定运动任务中的努力程度，以产生分级且更灵活的命令信号。方法。十四名健康人类受试者（九名男性，五名女性）通过在连续视觉反馈的指导下将手部测力计挤压到不同水平的预定力来对视觉线索做出反应，同时监测脑电图 (EEG) 和握力。提取和建模与运动相关的 EEG 特征以预测施加的力。主要结果。我们发现 EEG 的 8-30 Hz mu-beta 感觉运动节律的事件相关去同步化 (ERD) 可与不同程度的运动努力分开。在四重交叉验证后，发现线性分类器可以根据 ERD 向量预测握力，对于优势手和非优势手，受试者的平均准确度分别为 53% 和 55%。ERD 振幅随着目标力的增加而增加，但似乎通过了一个暗示非单调行为的低谷。意义。我们的结果表明，基于预期运动努力水平的建模和交互式反馈是可行的。观察到的 ERD 趋势表明，不同的机制可能会控制中等与低度和高度的运动努力。这可能在运动障碍的康复方案中有用。