Electrochemical measurements play a crucial role across various domains including air quality assessment, biological analysis, and the food industry. Miniaturized and power-efficient electrochemical potentiostats, facilitated by integrated circuits, have been instrumental in enabling wearable devices. However, the traditional CMOS potentiostat designs limit the simultaneous measurement of multi-sensor arrays within the same electrolyte solution where different bias potentials need to be applied to each sensor. Moreover, the utilization of modern integrated circuits with low supply voltage limits the applicability of electrochemical reactions requiring higher potential windows. This paper introduces an innovative potentiostat architecture that allows for sharing the reference electrode among multiple channels as well as expanding the voltage range for electrochemical cells. The novel potentiostat design is thoroughly described and results from a PCB prototype show strong output current linearity. Furthermore, verification of simultaneous measurements at different electrochemical bias conditions is presented and shown to match results from a commercial instrument.

Modern sensor technologies have been employed to monitor aspects of social interactions, such as human emotions, that are known to influence human health. To reduce the negative impact that some behaviors could have on our health, real-time monitoring of social interactions is desired to bring awareness of human behavior through real-time feedback. Still, the design of systems for the real-time monitoring of social interactions poses considerable challenges that range from multi-sensor integration to signal analysis. Intending to overcome these challenges, this paper presents a study of a variety of sensor modalities and the design of a multi-sensor framework that allows the study and real-time analysis of both in-person and virtual social interaction environments. The framework consists of three multi-sensor nodes and a central unit. Results show validation of the variety of sensor data collected from a single sensor node and behavioral information that can be identified due to data synchronization from multiple sensor nodes.

The field of human augmentation (HA) has received an increased research focus from neurotechnologists due to advancements in information sensing and processing capabilities. Despite advancements in HA, machine-to-human communication (MHC) is still reliant on visual and auditory pathways. Electrotactile stimulation of peripheral nervous system can be used as an alternative pathway for MHC, contingent upon consistently distinct electrotactile sensations can be created. A person can be trained to associate a distinct electrotactile sensation with a specific information cue, effectively creating an alternative pathway for MHC. A technique for creating distinct electrotactile stimulation is modulating the intensity of the observed electrotactile waveform. Among the possible techniques to modify intensity, this paper investigates the possibility of manipulating a train of high-frequency pulses with the duty cycle of a superimposed mask. Furthermore, this paper explores the limits of human ability to distinguish intensity levels created through manipulation of high-frequency pulses. This paper found that using the presented frequency manipulation technique makes it possible to reliably create 3 intensity levels with an accuracy higher than 85%.

High channel count neural implants that can record brain activities across diverse cortical regions represent the next step toward whole brain interfaces that will enable new understanding of brain operation and treatment of many neural disorders. To overcome the size and power constraints limiting the channel count of existing neural implants, this paper presents a new neural amplifier array design that utilizes hardware sharing to achieve low power and compact size. Moreover, to ease the burden of large volume data handling, in-situ data decimation is performed to enable off body evaluation of synchrony between signal pairs. A 32-channel analog front end array was designed and post-layout simulations show that the entire front end occupies only 0.031 mm2per channel while consuming only 3.34 μW per channel at 3.3 V in 0.5 μm CMOS. This front end decimates data by an order of magnitude while keeping the synchrony information with more than 89.1% accuracy.