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设计开发一种家用模块化脑机接口(BCI)平台,以颈髓损伤为例的研究。

Design-development of an at-home modular brain-computer interface (BCI) platform in a case study of cervical spinal cord injury.

机构信息

Department of Biomedical Engineering, University of Miami, 1251 Memorial Dr, MEA 204, Coral Gables, Miami, FL, 33146, USA.

Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.

出版信息

J Neuroeng Rehabil. 2022 Jun 3;19(1):53. doi: 10.1186/s12984-022-01026-2.

Abstract

OBJECTIVE

The objective of this study was to develop a portable and modular brain-computer interface (BCI) software platform independent of input and output devices. We implemented this platform in a case study of a subject with cervical spinal cord injury (C5 ASIA A).

BACKGROUND

BCIs can restore independence for individuals with paralysis by using brain signals to control prosthetics or trigger functional electrical stimulation. Though several studies have successfully implemented this technology in the laboratory and the home, portability, device configuration, and caregiver setup remain challenges that limit deployment to the home environment. Portability is essential for transitioning BCI from the laboratory to the home.

METHODS

The BCI platform implementation consisted of an Activa PC + S generator with two subdural four-contact electrodes implanted over the dominant left hand-arm region of the sensorimotor cortex, a minicomputer fixed to the back of the subject's wheelchair, a custom mobile phone application, and a mechanical glove as the end effector. To quantify the performance for this at-home implementation of the BCI, we quantified system setup time at home, chronic (14-month) decoding accuracy, hardware and software profiling, and Bluetooth communication latency between the App and the minicomputer. We created a dataset of motor-imagery labeled signals to train a binary motor imagery classifier on a remote computer for online, at-home use.

RESULTS

Average bluetooth data transmission delay between the minicomputer and mobile App was 23 ± 0.014 ms. The average setup time for the subject's caregiver was 5.6 ± 0.83 min. The average times to acquire and decode neural signals and to send those decoded signals to the end-effector were respectively 404.1 ms and 1.02 ms. The 14-month median accuracy of the trained motor imagery classifier was 87.5 ± 4.71% without retraining.

CONCLUSIONS

The study presents the feasibility of an at-home BCI system that subjects can seamlessly operate using a friendly mobile user interface, which does not require daily calibration nor the presence of a technical person for at-home setup. The study also describes the portability of the BCI system and the ability to plug-and-play multiple end effectors, providing the end-user the flexibility to choose the end effector to accomplish specific motor tasks for daily needs. Trial registration ClinicalTrials.gov: NCT02564419. First posted on 9/30/2015.

摘要

目的

本研究旨在开发一种与输入和输出设备无关的便携式、模块化脑机接口(BCI)软件平台。我们在一位颈脊髓损伤(C5 ASIA A)患者的病例研究中实现了这一平台。

背景

BCI 可以通过使用大脑信号来控制假肢或触发功能性电刺激,从而为瘫痪患者恢复独立性。尽管有几项研究已经成功地在实验室和家庭中实现了这项技术,但便携性、设备配置和护理人员设置仍然是限制其在家庭环境中部署的挑战。便携性对于将 BCI 从实验室过渡到家庭环境至关重要。

方法

BCI 平台的实现包括一个 Activa PC + S 发生器,发生器上有两个植入在感觉运动皮层的主导左手-手臂区域的四触点的硬膜下电极,一个固定在轮椅背面的小型计算机,一个定制的移动电话应用程序,以及一个作为末端效应器的机械手套。为了量化在家中实施 BCI 的性能,我们量化了在家中的系统设置时间、慢性(14 个月)解码精度、硬件和软件分析,以及 App 和小型计算机之间的蓝牙通信延迟。我们创建了一个运动想象标记信号的数据集,以在远程计算机上训练一个二进制运动想象分类器,用于在线、在家使用。

结果

小型计算机和移动 App 之间的平均蓝牙数据传输延迟为 23 ± 0.014 ms。受试者护理人员的平均设置时间为 5.6 ± 0.83 分钟。获取和解码神经信号以及将这些解码信号发送到末端效应器的平均时间分别为 404.1 ms 和 1.02 ms。经过 14 个月的训练,运动想象分类器的中位数精度为 87.5 ± 4.71%,无需重新训练。

结论

本研究提出了一种在家中使用的 BCI 系统的可行性,患者可以使用友好的移动用户界面无缝操作,该系统不需要日常校准,也不需要在家中设置时有人在场。本研究还描述了 BCI 系统的便携性,以及能够即插即用多个末端效应器的能力,为最终用户提供了选择末端效应器来完成日常需求的特定运动任务的灵活性。

试验注册

ClinicalTrials.gov:NCT02564419。首次于 2015 年 9 月 30 日公布。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e18/9166490/b5e732e9a82f/12984_2022_1026_Fig1_HTML.jpg

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