EEG-Controlled Functional Electrical Stimulation Therapy With Automated Grasp Selection: A Proof -of-Concept Study
EEG-Controlled Functional Electrical Stimulation Therapy With Automated Grasp Selection: A Proof -of-Concept Study
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DOI：
10.1310/sci2403-265
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发表时间：
2018-06-01
影响因子：
2.9
通讯作者：
Marquez-Chin, Cesar
中科院分区：
文献类型：
--
作者：
Likitlersuang, Jirapat;Koh, Ryan;Marquez-Chin, Cesar
背景资料：功能性电刺激治疗（FEST）是一种很有前途的颈髓损伤（SCI）后上肢功能恢复的干预措施。目的：本研究描述和评估了一种新的FEST系统，该系统旨在通过使用脑机接口（BCI）和计算机视觉（CV）模块，将自愿运动尝试和大规模的功能掌握实践结合起来。方法：基于单电极的脑机接口被用来检测运动启动尝试。CV系统识别目标对象并选择适当的抓取类型。将所需的抓握类型和触发命令发送到FES刺激器，其产生四种多通道肌肉刺激模式之一（精确、侧向、手掌或蚓状抓握）。该系统进行了评估与五个神经系统完整的参与者和一个参与者与完全的颈椎脊髓损伤。结果：建立了完整的BCI-CV-FES系统。当从一组八个对象中选择时，CV模块的总体分类准确率为90.8%。BCI模块触发所有参与者运动的平均延迟为5.9 +/- 1.5秒。对于单独SCI的参与者，CV准确率为87.5%，BCI潜伏期为5.3 +/- 9.4秒。结论：BCI和CV方法可以集成到FEST系统中，而不需要昂贵的资源或冗长的设置时间。其结果是一个临床相关的系统，旨在促进自愿运动的尝试和更多的重复不同的功能掌握在FEST。
Background: Functional electrical stimulation therapy (FEST) is a promising intervention for the restoration of upper extremity function after cervical spinal cord injury (SCI). Objectives: This study describes and evaluates a novel FEST system designed to incorporate voluntary movement attempts and massed practice of functional grasp through the use of brain-computer interface (BCI) and computer vision (CV) modules. Methods: An EEG-based BCI relying on a single electrode was used to detect movement initiation attempts. A CV system identified the target object and selected the appropriate grasp type. The required grasp type and trigger command were sent to an FES stimulator, which produced one of four multichannel muscle stimulation patterns (precision, lateral, palmar, or lumbrical grasp). The system was evaluated with five neurologically intact participants and one participant with complete cervical SCI. Results: An integrated BCI-CV-FES system was demonstrated. The overall classification accuracy of the CV module was 90.8%, when selecting out of a set of eight objects. The average latency for the BCI module to trigger the movement across all participants was 5.9 +/- 1.5 seconds. For the participant with SCI alone, the CV accuracy was 87.5% and the BCI latency was 5.3 +/- 9.4 seconds. Conclusion: BCI and CV methods can be integrated into an FEST system without the need for costly resources or lengthy setup times. The result is a clinically relevant system designed to promote voluntary movement attempts and more repetitions of varied functional grasps during FEST.