Wearable technology can be employed to elevate the abilities of humans to perform demanding and complex tasks more efficiently. Armbands capable of surface electromyography (sEMG) are attractive and noninvasive devices from which human intent can be derived by leveraging machine learning. However, the sEMG acquisition systems currently available tend to be prohibitively costly for personal use or sacrifice wearability or signal quality to be more affordable. This work introduces the 3DC Armband designed by the Biomedical Microsystems Laboratory in Laval University; a wireless, 10-channel, 1000 sps, dry-electrode, low-cost (∼150 USD) myoelectric armband that also includes a 9-axis inertial measurement unit. The proposed system is compared with the Myo Armband by Thalmic Labs, one of the most popular sEMG acquisition systems. The comparison is made by employing a new offline dataset featuring 22 able-bodied participants performing eleven hand/wrist gestures while wearing the two armbands simultaneously. The 3DC Armband systematically and significantly ( $p<0.05$ ) outperforms the Myo Armband, with three different classifiers employing three different input modalities when using ten seconds or more of training data per gesture. This new dataset, alongside the source code, Altium project and 3-D models are made readily available for download within a Github repository.

Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the off line accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between off line and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different-recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly (p <; 0.05) outperforms using fine-tuning as the recalibration technique.

Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.

This paper presents a 0.13- $\mu \text{m}$ CMOS system-on-chip (SoC) for simultaneous multichannel optogenetics and multichannel neural recording in freely moving laboratory animals. This fully integrated system provides 10 multimodal recording channels with analog-to-digital conversion and a four- channel LED driver circuit for optogenetic stimulation. The bio-amplifier design includes a programmable bandwidth (BW) (0.5 Hz–7 kHz) to collect either the action potentials (APs) and/or the local field potentials (LFPs) and has a noise efficiency factor (NEF) of 2.30 for an input-referred noise of 3.2 $\mu V_{\text {rms}}$ within a BW of 10–7 kHz. The low-power delta–sigma ( $\Delta \Sigma $ ) MASH 1-1-1 analog-to-digital converter (ADC) is designed to work at low oversampling ratios (OSRs) (≤50) and has an effective number of bits (ENOB) of 9.75 bits at an OSR of 25 (BW of 10 kHz). The utilization of a $\Delta \Sigma $ ADC is the key to address the flexibility needed to address different noise versus power consumption tradeoff of various experimental settings. It leverages a new technique that reduces its size by subtracting the output of each $\Delta \Sigma $ branch in the digital domain, instead of in the analog domain as done conventionally. The ADC is followed by an on-chip fourth-order cascaded integrator-comb (CIC4) decimation filter (DF). A whole recording channel, including the bio-amplifier, the $\Delta \Sigma $ MASH 1-1-1, and the DF consumes 11.2 $\mu \text{W}$ . Optical stimulation is performed with an LED driver using a regulated cascode current source with feedback that can accommodate a wide range of LED parameters and battery voltages. The SoC is validated in vivo within a wireless experimental platform in both the ventral posteromedial nucleus (VPM) and cerebral motor cortex brain regions of a virally mediated Channelrhodopsin-2 (ChR2) rat.

Rapid, high-sensitivity, and real-time characterization of microorganisms plays a significant role in several areas, including clinical diagnosis, human healthcare, early detection of outbreaks, and the protection of living beings. Integrating microbiology and electrical engineering promises the development of low-cost, miniaturized, autonomous, and high-sensitivity sensors to quantify and characterize bacterial strains at various concentrations. Electrochemical-based biosensors are receiving particular attention in microbiological applications among the different biosensing devices. Several approaches have been adopted to design and fabricate cutting-edge, miniaturized, and portable electrochemical biosensors to track and monitor bacterial cultures in real time. These techniques differ in their sensing interface circuits and microelectrode fabrication. The goals of this review are (i) to summarize the current state of CMOS sensing circuit designs in label-free electrochemical biosensors for bacteria monitoring and (ii) to discuss the material and size of the electrodes used in electrochemical biosensors in microbiological applications. In this paper, we reviewed the latest and most advanced CMOS integrated interface circuits that have recently been used in electrochemical biosensors to identify and characterize bacteria species, such as impedance spectroscopy, capacitive, amperometry, and voltammetry, etc. In addition to the interface circuit design, other crucial factors, such as the material and scale of the electrodes, must be considered to increase the sensitivity of electrochemical biosensors. Surveying the literature in this field improves our knowledge about the impact of electrode designs and materials on sensing precision and will help future designers adapt, design, and fabricate appropriate electrode configurations based on their application. Thus, we summarized the conventional microelectrode designs and materials mainly employed in microbial sensors, including interdigitated electrodes (IDEs), microelectrode arrays (MEAs), paper, and carbon-based electrodes, etc.

We present a small and lightweight fully wireless optogenetic headstage capable of optical neural stimulation and electrophysiological recording. The headstage is suitable for conducting experiments with small transgenic rodents, and features two implantable fiber-coupled light-emitting diode (LED) and two electrophysiological recording channels. This system is powered by a small lithium-ion battery and is entirely built using low-cost commercial off-the-shelf components for better flexibility, reduced development time and lower cost. Light stimulation uses customizable stimulation patterns of varying frequency and duty cycle. The optical power that is sourced from the LED is delivered to target light-sensitive neurons using implantable optical fibers, which provide a measured optical power density of 70 mW/mm² at the tip. The headstage is using a novel foldable rigid-flex printed circuit board design, which results into a lightweight and compact device. Recording experiments performed in the cerebral cortex of transgenic ChR2 mice under anesthetized conditions show that the proposed headstage can trigger neuronal activity using optical stimulation, while recording microvolt amplitude electrophysiological signals.