BACKGROUND After stroke, wrist extension dyscoordination precludes functional arm/hand. We developed a more spatially precise brain signal for use in brain computer interface (BCI's) for stroke survivors. NEW METHOD Combination BCI protocol of real-time functional magnetic resonance imaging (rt-fMRI) sequentially followed by functional near infrared spectroscopy (rt-fNIRS) neurofeedback, interleaved with motor learning sessions without neural feedback. Custom Matlab and Python code was developed to provide rt-fNIRS-based feedback to the chronic stroke survivor, system tester. RESULTS . The user achieved a maximum of 71% brain signal accuracy during rt-fNIRS neural training; progressive focus of brain activation across rt-fMRI neural training; increasing trend of brain signal amplitude during wrist extension across rt-fNIRS training; and clinically significant recovery of arm coordination and active wrist extension. COMPARISON WITH EXISTING METHODS: . Neurorehabilitation, peripherally directed, shows limited efficacy, as do EEG-based BCIs, for motor recovery of moderate/severely impaired stroke survivors. EEG-based BCIs are based on electrophysiological signal; whereas, rt-fMRI and rt-fNIRS are based on neurovascular signal. CONCLUSION . The system functioned well during user testing. Methods are detailed for others' use. The system user successfully engaged rt-fMRI and rt-fNIRS neurofeedback systems, modulated brain signal during rt-fMRI and rt-fNIRS training, according to volume of brain activation and intensity of signal, respectively, and clinically significant improved limb coordination and active wrist extension. fNIRS use in this case demonstrates a feasible/practical BCI system for further study with regard to use in chronic stroke rehab, and fMRI worked in concept, but cost and patient-friendly issues make it less feasible for clinical practice.

中文翻译：

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开发一种组合的，顺序的实时功能磁共振成像和fNIRS神经反馈系统，以增强中风后的运动学习能力。

背景技术在中风之后，腕部伸展失调会妨碍功能性的手臂/手。我们开发了一种在空间上更精确的大脑信号，用于中风幸存者的大脑计算机接口（BCI）。新方法实时功能磁共振成像（rt-fMRI）的BCI协议的组合，其后依次是功能近红外光谱（rt-fNIRS）神经反馈，与无神经反馈的运动学习会话交错。开发了自定义Matlab和Python代码，以向慢性卒中幸存者，系统测试人员提供基于rt-fNIRS的反馈。结果。在rt-fNIRS神经训练过程中，用户达到了最高71％的大脑信号准确度；跨rt-fMRI神经训练逐渐激活大脑 在rt-fNIRS训练过程中，手腕伸展过程中脑信号振幅的趋势不断增加；以及手臂协调和活动腕部伸展的临床显着恢复。与现有方法的比较：。与基于EEG的BCI一样，周围神经康复对中度/重度中风幸存者运动恢复的疗效有限。基于EEG的BCI基于电生理信号。rt-fMRI和rt-fNIRS是基于神经血管信号的。结论。该系统在用户测试期间运行良好。详细说明了供他人使用的方法。该系统用户成功地使用了rt-fMRI和rt-fNIRS神经反馈系统，分别根据大脑激活的量和信号强度，在rt-fMRI和rt-fNIRS训练期间调节了脑信号，并且在临床上显着改善了肢体协调性和活动手腕延期。