Accurately assessing mental workload is crucial in cognitive neuroscience, human-computer interaction, and real-time monitoring, as cognitive load fluctuations affect performance and decision-making. While Electroencephalography (EEG)-based machine learning (ML) models can be used to this end, their high computational cost hinders embedded real-time applications. Hardware implementations of spiking neural networks (SNNs) offer a promising alternative for low-power, fast, event-driven processing. This study compares hardware-compatible SNN models with various traditional ML ones, using an open-source multimodal dataset. Our results show that multimodal integration improves accuracy, with SNN performance comparable to the ML one, demonstrating their potential for real-time implementations of cognitive load detection. These findings position event-based processing as a promising solution for low-latency, energy-efficient workload monitoring in adaptive closed-loop embedded devices that dynamically regulate cognitive load.

The electromyography (EMG) signal is particularly useful in monitoring muscle activity, and it can be acquired noninvasively on the skin surface. Thanks to these key characteristics, EMG-based human–machine interfaces (HMIs) for prosthetic myocontrol, as well as gesture recognition, are becoming widespread. A key challenge in this context is to design embedded systems to process EMG signals and generate motor commands with miniaturized, unobtrusive, and low-power devices, reliably and in real time, at a relatively low cost to provide continuous monitoring without causing stigma or discomfort. This article presents an in-depth review of the current status and future research challenges in systems and circuits for EMG acquisition and processing. We start by illustrating the sensor interfaces and acquisition systems required for signal analysis to provide efficient and effective ways of understanding the signal and its nature. We, then, focus on conventional state-of-the-art (SoA) EMG gesture recognition algorithms as well as novel architectures that tackle EMG processing challenges, i.e., hyperdimensional computing (HDC), blind source separation (BSS), and spiking neural networks (SNNs). Finally, we discuss open challenges, such as EMG variability, natural control, and efficient computation, to bring the myocontrol completely out of the laboratory, filling the gap between research prototypes and real-world applications.

Mixed signal neuromorphic circuits represent a promising technology for implementing compact and ultra-low power prosthetic devices that can be directly interfaced to living tissue. However, to accurately emulate the dynamical behavior of the biological tissue, it is necessary to determine the optimal set of specifications and bias parameters for these circuits. In this paper we show how this can be done for a silicon neuron design, by applying a statistical Data Assimilation method (DA). We present a conductance-based silicon neuron based on the Mahowald-Douglas (MD) design and use the DA method to estimate its state variables and the ion channels parameters, so that it can accurately emulate the properties of biological neurons involved in the Central Pattern Generators (CPGs) responsible for producing the respiratory and heart-rate rhythms. While previous work has shown how DA well-estimates and predicts parameters from membrane voltage measurements using a semi-empirical Hodgkin-Huxley neural model, here we show how the same method is suitable for simplified Very Large Scale Integration (VLSI) circuit designs and demonstrate how it allows us to reliably predict the response of the MD neuron to different input current profiles.

Due to extreme and unpredictable conditions, oceanic missions are still a persistent challenge in robotics. With the aim of improving decision autonomy and robustness against unforeseen circumstances, the EU-funded CoCoRo project is developing a cognitive swarm of underwater robots. Swarm and cognition algorithms will be studied and validated with a large number of miniaturized and affordable AUVs, named Jeff, whose custom mechanical design is described in this paper. Jeff is conceived for high-mobility in 3D cluttered environments and has distributed sensors for multi-directional perception and communication. The propulsion and the buoyancy systems are designed with watertight and energetically efficient solutions to improve system reliability and energetic autonomy. The manuscript also describes the design of a docking system that allows Jeff to passively align and connect to a submerged docking station for battery charging.