  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, 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 with custom fully dry soft electrodes
to enhance comfort for long wear. 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 uJ. 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 24 uJ per inference and a total
system power of only 16.28 mW, allowing for continuous operation of more than
12 h with a small 75 mAh battery.
