Behboodi Ahad, Kline Julia, Gravunder Andrew, Phillips Connor, Parker Sheridan M, Damiano Diane L
Department of Biomechanics, University of Nebraska Omaha, Omaha, NE, United States.
Neurorehabilitation and Biomechanics Research Section, Rehabilitation Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD, United States.
Front Hum Neurosci. 2024 Apr 3;18:1346050. doi: 10.3389/fnhum.2024.1346050. eCollection 2024.
In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations. First, in a cohort of eight healthy volunteers, we successfully implemented a BCI-NFT system based on detection of slow movement-related cortical potentials (MRCP) from EEG generated by attempted dorsiflexion to simultaneously activate Neuromuscular Electrical Stimulation which assisted movement and served to enhance sensory feedback to the sensorimotor cortex. Participants also viewed a computer display that provided real-time visual feedback of ankle range of motion with an individualized target region displayed to encourage maximal effort. After evaluating several potential strategies, we employed a Long short-term memory (LSTM) neural network, a deep learning algorithm, to detect the motor intent prior to movement onset. We then evaluated the system in a 10-session ankle dorsiflexion training protocol on a child with CP. By employing transfer learning across sessions, we could significantly reduce the number of calibration trials from 50 to 20 without compromising detection accuracy, which was 80.8% on average. The participant was able to complete the required calibration trials and the 100 training trials per session for all 10 sessions and post-training demonstrated increased ankle dorsiflexion velocity, walking speed and step length. Based on exceptional system performance, feasibility and preliminary effectiveness in a child with CP, we are now pursuing a clinical trial in a larger cohort of children with CP.
在运动康复领域,脑机接口神经反馈训练(BCI-NFT)成为一种很有前景的策略。其目的是利用个体的大脑活动来刺激或辅助运动,从而强化感觉运动通路并促进运动恢复。采用各种方法,BCI-NFT已被证明对增强中风患者主要是上肢的运动功能有效,而在脑瘫(CP)方面的研究报道很少。我们的主要目标是开发一种基于脑电图(EEG)的BCI-NFT系统,采用联想学习范式,以改善CP患者以及可能其他神经疾病人群对踝关节背屈的选择性控制。首先,在一组八名健康志愿者中,我们成功实施了一个基于检测脑电图中由尝试背屈产生的与慢运动相关的皮层电位(MRCP)的BCI-NFT系统,以同时激活神经肌肉电刺激,辅助运动并增强对感觉运动皮层的感觉反馈。参与者还观看了一个计算机显示屏,该显示屏提供踝关节运动范围的实时视觉反馈,并显示个性化目标区域以鼓励最大程度的努力。在评估了几种潜在策略后,我们采用了长短期记忆(LSTM)神经网络,一种深度学习算法,来在运动开始前检测运动意图。然后,我们在一名CP儿童的10节踝关节背屈训练方案中对该系统进行了评估。通过在各节训练中采用迁移学习,我们能够在不影响检测准确率的情况下,将校准试验次数从50次显著减少到20次,平均检测准确率为80.8%。该参与者能够完成所需的校准试验以及所有10节训练课中每节的100次训练试验,训练后踝关节背屈速度、步行速度和步长均有所增加。基于在一名CP儿童中出色的系统性能、可行性和初步有效性,我们现在正在更大的CP儿童队列中开展一项临床试验。