A low-cost transhumeral prosthesis operated via an ML-assisted EEG-head gesture control system.

Benjamin J Choi, Ji Liu
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Abstract

Objective.Key challenges in upper limb prosthetics include a lack of effective control systems, the often invasive surgical requirements of brain-controlled limbs, and prohibitive costs. As a result, disuse rates remain high despite potential for increased quality of life. To address these concerns, this project developed a low cost, noninvasive transhumeral neuroprosthesis-operated via a combination of electroencephalography (EEG) signals and head gestures.Approach.To address the shortcomings of current noninvasive neural monitoring techniques-namely, single-channel EEG-we leveraged machine learning (ML), creating a neural network-based EEG interpretation algorithm. ML generation was guided by two underlying goals: (1) to improve overall system performance by combining discrete models using a prediction voting scheme, and (2) to favor modeldiversitywithin these new neural network ensembles, as opposed to individual modelperformance. EEG data from eight frequency bands was collected from human subjects to train a ML algorithm employing a hierarchical mixture-of-experts structure. We also implemented head gesture-based control to assist in the generation of additional stable classes for the control system.Main results.The final model performs competitively with existing EEG interpretation systems. Inertial measurement unit (IMU)-based head gestures supplement the neural control system, with 270° actuation of synovial elbow and radial wrist joints driven by intuitive corresponding head gestures. The brain-controlled prosthesis presented in this study costs US$300 to manufacture and achieved competitive performance on a Box and Block Test.Significance.These results suggest proof-of-concept for potential application as an alternative to current prosthetics, but it is important to note that the demonstration in this study remains exploratory. Future work includes broader clinical testing and exploring further uses for the developed ML system.

通过机器学习辅助脑电图-头部手势控制系统操作的低成本肱骨假体。
目的:上肢假肢面临的主要挑战包括缺乏有效的控制系统,脑控肢体的手术要求往往是侵入性的,以及高昂的成本。因此,尽管有可能提高生活质量,但废弃率仍然很高。为了解决这些问题,该项目开发了一种低成本、无创的经肱骨神经假体——通过脑电图(EEG)信号和头部手势的组合来操作。方法 ;为了解决当前无创神经监测技术(即单通道脑电图)的缺点,我们利用机器学习(ML),创建了基于神经网络的EEG解释算法。机器学习生成由两个潜在目标指导:(1)通过使用预测投票方案组合离散模型来提高整体系统性能;(2)在这些新的神经网络集成中支持模型多样性,而不是单个模型性能。从人类受试者身上收集8个频段的脑电图数据,采用分层混合专家(MoE)结构训练机器学习算法。我们还实现了基于头部手势的控制,以帮助为控制系统生成额外的稳定类。 ;主要结果 ;基于imu的头部手势补充了神经控制系统,通过直观的相应头部手势驱动滑膜肘关节和桡腕关节270°驱动。本研究中提出的脑控假体制造成本为300美元,并在盒子和块测试(BBT)中取得了具有竞争力的表现。 ;意义 ;这些结果表明,作为现有义肢的替代方案,它可能会有潜在的应用,但重要的是要注意,本研究中的论证仍然是探索性的。未来的工作包括更广泛的临床测试和探索开发的ML系统的进一步用途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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