Frey Sebastian, Lucchini Mattia Alberto, Kartsch Victor, Ingolfsson Thorir Mar, Bernardi Andrea Helga, Segessenmann Michael, Osieleniec Jakub, Benatti Simone, Benini Luca, Cossettini Andrea
IEEE Trans Biomed Circuits Syst. 2025 Jun;19(3):616-628. doi: 10.1109/TBCAS.2024.3478798.
Recent advancements in head-mounted wearable technology are revolutionizing the field of biopotential measurement, but the integration of these technologies into practical, user-friendly devices remains challenging due to issues with design intrusiveness, comfort, reliability, and data privacy. To address these challenges, this paper presents GAPses, a novel smart glasses platform designed for unobtrusive, comfortable, and secure acquisition and processing of electroencephalography (EEG) and electrooculography (EOG) signals. We introduce a direct electrode-electronics interface within a sleek frame design, with custom fully dry soft electrodes to enhance comfort for long wear. The fully assembled glasses, including electronics, weigh 40 g and have a compact size of 160 mm x 145 mm. An integrated parallel ultra-low-power RISC-V processor (GAP9, Greenwaves Technologies) processes data at the edge, thereby eliminating the need for continuous data streaming through a wireless link, enhancing privacy, and increasing system reliability in adverse channel conditions. We demonstrate the broad applicability of the designed prototype through validation in a number of EEG-based interaction tasks, including alpha waves, steady-state visual evoked potential analysis, and motor movement classification. Furthermore, we demonstrate an EEG-based biometric subject recognition task, where we reach a sensitivity and specificity of 98.87% and 99.86% respectively, with only 8 EEG channels and an energy consumption per inference on the edge as low as 121 $\mu$J. Moreover, in an EOG-based eye movement classification task, we reach an accuracy of 96.68% on 11 classes, resulting in an information transfer rate of 94.78 bit/min, which can be further increased to 161.43 bit/min by reducing the accuracy to 81.43%. The deployed implementation has an energy consumption of 40 $\mu$J per inference and a total system power of only 12.4 mW, of which only 1.61% is used for classification, allowing for continuous operation of more than 22 h with a small 75 mAh battery.
头戴式可穿戴技术的最新进展正在彻底改变生物电位测量领域，但由于设计的侵入性、舒适性、可靠性和数据隐私等问题，将这些技术集成到实用、用户友好的设备中仍然具有挑战性。为了应对这些挑战，本文介绍了GAPses，这是一种新颖的智能眼镜平台，旨在以不引人注意、舒适且安全的方式采集和处理脑电图（EEG）和眼电图（EOG）信号。我们在时尚的框架设计中引入了直接电极 - 电子接口，配备定制的全干式软电极，以提高长时间佩戴的舒适度。包括电子设备在内的完整组装眼镜重40克，尺寸紧凑，为160毫米×145毫米。集成的并行超低功耗RISC - V处理器（GAP9，Greenwaves Technologies）在边缘进行数据处理，从而无需通过无线链路持续传输数据，增强了隐私性，并提高了在恶劣信道条件下的系统可靠性。我们通过在一些基于EEG的交互任务中进行验证，展示了所设计原型的广泛适用性，这些任务包括阿尔法波、稳态视觉诱发电位分析和运动动作分类。此外，我们展示了一项基于EEG的生物特征主体识别任务，在该任务中，仅使用8个EEG通道，边缘每推理一次的能耗低至121 μJ，我们分别达到了98.87%的灵敏度和99.86%的特异性。此外，在基于EOG的眼动分类任务中，我们在11个类别上达到了96.68%的准确率，信息传输速率为94.78比特/分钟，通过将准确率降低到81.43%，该速率可进一步提高到161.43比特/分钟。所部署的实现每推理一次的能耗为40 μJ，系统总功耗仅为12.4毫瓦，其中仅1.61%用于分类，使用一个75毫安的小电池可实现超过22小时的连续运行。
