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.

中文翻译：

---

脑瘫儿童神经康复运动尝试期间具有实时脑电图检测和电刺激辅助的 BCI 神经反馈系统的开发和评估

在运动康复领域，脑机接口神经反馈训练（BCI-NFT）成为一种有前景的策略。其目的是利用个人的大脑活动来刺激或协助运动，从而加强感觉运动通路并促进运动恢复。采用各种方法，BCI-NFT 已被证明可有效增强中风时主要上肢的运动功能，但在脑瘫 (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 和其他潜在神经群体对踝关节背屈的选择性控制。首先，在由 8 名健康志愿者组成的队列中，我们成功实施了 BCI-NFT 系统，该系统基于尝试背屈时产生的脑电图检测慢速运动相关皮层电位 (MRCP)，同时激活神经肌肉电刺激，从而辅助运动并增强运动能力。感觉运动皮层的感觉反馈。参与者还观看了计算机显示器，该显示器提供脚踝运动范围的实时视觉反馈，并显示个性化的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）来检测运动开始之前的运动意图。然后，我们通过对患有 CP 的儿童进行 10 次踝关节背屈训练方案来评估该系统。通过跨会话采用迁移学习，我们可以将校准试验的数量从 50 次显着减少到 20 次，而不会影响检测精度（平均为 80.8%）。参与者能够完成所有 10 次训练所需的校准试验和每次训练 100 次训练试验，并且训练后证明踝关节背屈速度、步行速度和步长有所增加。基于出色的系统性能、可行性和对 CP 儿童的初步有效性，我们现在正在更大范围的 CP 儿童中进行临床试验。