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Peripheral nerve interfacing plays a crucial role in various healthcare applications. Generally, interfacing peripheral nerves results in a compromise between selectivity and invasiveness. In particular, large nerves carry many axonal fibers, which are difficult to address selectively without penetrating the nerve. Higher selectivity without nerve penetration can be achieved by targeting small nerves with extraneural cuff electrodes. However, in practice, small nerves are challenging to interface appropriately. Herein, a new multielectrode device is presented that can selectively interface small nerves (

Abstract Thin film electronic devices based on flexible biocompatible substrates are desired in various fields such as implants, soft robotics, and wearables, where stretchability is often necessary. Structure‐enabled stretchability in flexible thin films can be achieved by introducing origami‐inspired folds, thereby storing excess material in the out‐of‐plane direction to unfold upon stress. When using vapor‐deposited substrates such as parylene‐C, folds can be introduced prefabrication using molds patterned in repeated grooves and ridges. Here, this work reports the fabrication and parametrization of 10‐µm‐thick stretchable origami parylene‐C electrodes using 3D printed molds. The molds are printed with a sinusoidal pattern and tunable amplitude and slope, with accurate printing results up to 60° steepness. A 160‐nm‐thick gold layer on top of the folded parylene is patterned via laser ablation following the 3D mold shape. Depending on the design parameters, the resulting electrodes maintain functionality until 40%–100% strain. By 3D printing the molds, this technique can fabricate electrodes with complete control of the designed directions of stretchability in a rapid prototyping approach.

Abstract Activity of an innervated tissue can be modulated based on an acquired biomarker through feedback loops. How to convert this biomarker into a meaningful stimulation pattern is still a topic of intensive research. In this article, we present a simple closed-loop mechanism to control the mean angle of a locust’s leg in real time by modulating the frequency of the stimulation on its extensor motor nerve. The nerve is interfaced with a custom-designed cuff electrode and the feedback loop is implemented online with a proportional control algorithm, which runs solely on a microcontroller without the need of an external computer. The results show that the system can be controlled with a single-input, single-output feedback loop. The model described in this article can serve as a primer for young researchers to learn about neural control in biological systems before applying these concepts in advanced systems. We expect that the approach can be advanced to achieve control over more complex movements by increasing the number of recorded biomarkers and selective stimulation units.

Virtual reality environments offer great opportunities to study the performance of brain-computer interfaces (BCIs) in real-world contexts. As real-world stimuli are typically multimodal, their neuronal integration elicits complex response patterns. To investigate the effect of additional auditory cues on the processing of visual information, we used virtual reality to mimic safety-related events in an industrial environment while we concomitantly recorded electroencephalography (EEG) signals. We simulated a box traveling on a conveyor belt system where two types of stimuli – an exploding and a burning box – interrupt regular operation. The recordings from 16 subjects were divided into two subsets, a visual-only and an audio-visual experiment. In the visual-only experiment, the response patterns for both stimuli elicited a similar pattern – a visual evoked potential (VEP) followed by an event-related potential (ERP) over the occipital-parietal lobe. Moreover, we found the perceived severity of the event to be reflected in the signal amplitude. Interestingly, the additional auditory cues had a twofold effect on the previous findings: The P1 component was significantly suppressed in the case of the exploding box stimulus, whereas the N2c showed an enhancement for the burning box stimulus. This result highlights the impact of multisensory integration on the performance of realistic BCI applications. Indeed, we observed alterations in the offline classification accuracy for a detection task based on a mixed feature extraction (variance, power spectral density, and discrete wavelet transform) and a support vector machine classifier. In the case of the explosion, the accuracy slightly decreased by –1.64% p. in an audio-visual experiment compared to the visual-only. Contrarily, the classification accuracy for the burning box increased by 5.58% p. when additional auditory cues were present. Hence, we conclude, that especially in challenging detection tasks, it is favorable to consider the potential of multisensory integration when BCIs are supposed to operate under (multimodal) real-world conditions.