3D printed on-skin electrodes are of notable interest because, unlike traditional wet silver/silver chloride (Ag/AgCl) on-skin electrodes, they can be personalized and 3D printed using a variety of materials with distinct properties such as stretchability, conformal interfaces with skin, biocompatibility, wearable comfort, and, finally, low-cost manufacturing. Dry on-skin electrodes, in particular, have the additional advantage of replacing electrolyte gel, which dehydrates and coagulates with prolonged use. However, issues arise in performance optimization with the recently discovered dry materials. These challenges become even more critical when the on-skin electrodes are scaled down to a miniaturized size, making the detection of various biosignals while keeping mechanical resilience under several conditions crucial. Thus, this thesis focuses on designing, fabricating, optimizing, and applying a personalized, fully 3D-printed permeable skin patch with miniaturized octopus-like suckers and embedded microchannels for enhanced mechanical strengths, breathability, and biosignal monitoring. The developed device showcases a rapid, cost-effective fabrication process of porous skin patches and the printing process of ink metal-based materials that expands its applications to low-resource settings and environments.

Objective. Dry electrodes have an advantage over gel-based 'wet' electrodes by providing quicker set-up time for electroencephalography recording; however, the potentially poorer contact can result in noisier recordings. We examine the impact that this may have on brain-computer interface communication and potential approaches for mitigation. Approach. We present a performance comparison of wet and dry electrodes for use with the P300 speller system in both healthy participants and participants with communication disabilities (ALS and PLS), and investigate the potential for a data-driven dynamic data collection algorithm to compensate for the lower signal-to-noise ratio (SNR) in dry systems. Main results. Performance results from sixteen healthy participants obtained in the standard static data collection environment demonstrate a substantial loss in accuracy with the dry system. Using a dynamic stopping algorithm, performance may have been improved by collecting more data in the dry system for ten healthy participants and eight participants with communication disabilities; however, the algorithm did not fully compensate for the lower SNR of the dry system. An analysis of the wet and dry system recordings revealed that delta and theta frequency band power (0.1-4 Hz and 4-8 Hz, respectively) are consistently higher in dry system recordings across participants, indicating that transient and drift artifacts may be an issue for dry systems. Significance. Using dry electrodes is desirable for reduced set-up time; however, this study demonstrates that online performance is significantly poorer than for wet electrodes for users with and without disabilities. We test a new application of dynamic stopping algorithms to compensate for poorer SNR. Dynamic stopping improved dry system performance; however, further signal processing efforts are likely necessary for full mitigation.