Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, an affordable, non-invasive neuro-driven prosthetic arm system. MindArm employs a deep neural network (DNN) to translate brain signals, captured by low-cost surface electroencephalogram (EEG) electrodes, into prosthetic arm movements. Utilizing an Open Brain Computer Interface and UDP networking for signal processing, the system seamlessly controls arm motion. In the compute module, we run a trained DNN model to interpret filtered micro-voltage brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. Experimental results from a fully functional prototype show high accuracy across three actions, with 91% for idle/stationary, 85% for handshake, and 84% for cup pickup. The system costs approximately $500-550, including $400 for the EEG headset and $100-150 for motors, 3D printing, and assembly, offering an affordable alternative for mind-controlled prosthetic devices.

翻译：目前，上肢活动能力受损者（以下简称"患者"）面临的技术解决方案十分有限，这主要源于两大挑战：(1) 非侵入式假肢设备通常价格昂贵且维护成本高昂；(2) 侵入式解决方案需要进行高风险、高成本的脑部手术，可能带来健康风险。因此，现有技术方案难以覆盖不同经济背景的所有患者。为此，我们提出一种名为MindArm的低成本技术解决方案——一套经济实惠的非侵入式神经驱动假肢手臂系统。该系统采用深度神经网络（DNN）解析由低成本表面脑电图（EEG）电极采集的脑电信号，并将其转换为假肢手臂动作。通过开放式脑机接口与UDP网络协议进行信号处理，系统能够无缝控制手臂运动。在计算模块中，我们运行训练好的DNN模型来解读滤波后的微电压脑电信号，继而通过串行通信将其无缝转化为假肢手臂动作。全功能原型机的实验结果显示，系统在三种动作模式下均具有较高准确率：空闲/静止状态达91%，握手动作达85%，抓取水杯动作达84%。该系统总成本约为500-550美元（其中400美元用于EEG头戴设备，100-150美元用于电机、3D打印及组装），为意念控制假肢设备提供了经济可行的替代方案。