Agashe H A, Paek A Y, Contreras-Vidal J L
Noninvasive Brain-Machine Interface Systems Lab, University of Houston, Houston, TX, United States.
Noninvasive Brain-Machine Interface Systems Lab, University of Houston, Houston, TX, United States.
Prog Brain Res. 2016;228:107-28. doi: 10.1016/bs.pbr.2016.04.016. Epub 2016 Jun 10.
Upper limb amputation results in a severe reduction in the quality of life of affected individuals due to their inability to easily perform activities of daily living. Brain-machine interfaces (BMIs) that translate grasping intent from the brain's neural activity into prosthetic control may increase the level of natural control currently available in myoelectric prostheses. Current BMI techniques demonstrate accurate arm position and single degree-of-freedom grasp control but are invasive and require daily recalibration. In this study we tested if transradial amputees (A1 and A2) could control grasp preshaping in a prosthetic device using a noninvasive electroencephalography (EEG)-based closed-loop BMI system. Participants attempted to grasp presented objects by controlling two grasping synergies, in 12 sessions performed over 5 weeks. Prior to closed-loop control, the first six sessions included a decoder calibration phase using action observation by the participants; thereafter, the decoder was fixed to examine neuroprosthetic performance in the absence of decoder recalibration. Ability of participants to control the prosthetic was measured by the success rate of grasping; ie, the percentage of trials within a session in which presented objects were successfully grasped. Participant A1 maintained a steady success rate (63±3%) across sessions (significantly above chance [41±5%] for 11 sessions). Participant A2, who was under the influence of pharmacological treatment for depression, hormone imbalance, pain management (for phantom pain as well as shoulder joint inflammation), and drug dependence, achieved a success rate of 32±2% across sessions (significantly above chance [27±5%] in only two sessions). EEG signal quality was stable across sessions, but the decoders created during the first six sessions showed variation, indicating EEG features relevant to decoding at a smaller timescale (100ms) may not be stable. Overall, our results show that (a) an EEG-based BMI for grasping is a feasible strategy for further investigation of prosthetic control by amputees, and (b) factors that may affect brain activity such as medication need further examination to improve accuracy and stability of BMI performance.
上肢截肢会导致受影响个体的生活质量严重下降，因为他们无法轻松进行日常生活活动。将大脑神经活动中的抓握意图转化为假肢控制的脑机接口（BMI），可能会提高目前肌电假肢的自然控制水平。当前的BMI技术能够实现精确的手臂位置和单自由度抓握控制，但具有侵入性且需要每日重新校准。在本研究中，我们测试了经桡骨截肢者（A1和A2）是否能够使用基于无创脑电图（EEG）的闭环BMI系统来控制假肢中的抓握预成型。参与者在5周内进行的12次实验中，试图通过控制两种抓握协同动作来抓取呈现的物体。在闭环控制之前，前六次实验包括一个解码器校准阶段，参与者通过动作观察来进行校准；此后，解码器固定下来，以检查在不解码器重新校准的情况下神经假肢的性能。通过抓握成功率来衡量参与者控制假肢的能力；即，在一次实验中成功抓取呈现物体的试验次数所占的百分比。参与者A1在各次实验中保持了稳定的成功率（63±3%）（在11次实验中显著高于随机水平[41±5%]）。参与者A2正在接受治疗抑郁症、激素失衡、疼痛管理（针对幻肢痛以及肩关节炎症）和药物依赖的药物治疗，其在各次实验中的成功率为32±2%（仅在两次实验中显著高于随机水平[27±5%]）。各次实验中的脑电图信号质量稳定，但在前六次实验中创建的解码器显示出变化，这表明与较小时间尺度（100毫秒）解码相关的脑电图特征可能不稳定。总体而言，我们的结果表明：（a）基于脑电图的抓握BMI是截肢者进一步研究假肢控制的可行策略；（b）可能影响大脑活动的因素，如药物，需要进一步研究以提高BMI性能的准确性和稳定性。
