EMG) And Electroencephalography Research Articles

Machine Learning Predicts Phenoconversion from Polysomnography in Isolated REM Sleep Behavior Disorder.

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of alpha-synucleinopathies. This study aimed at developing a fully-automated machine learning framework for the prediction of phenoconversion in patients with iRBD by using data recorded during polysomnography (PSG). A total of 66 patients with iRBD were included, of whom 18 converted to an overt alpha-synucleinopathy within 2.7 ± 1.0 years. For each patient, a baseline PSG was available. Sleep stages were scored automatically, and time and frequency domain features were derived from electromyography (EMG) and electroencephalography (EEG) signals in REM and non-REM sleep. Random survival forest was employed to predict the time to phenoconversion, using a four-fold cross-validation scheme and by testing several combinations of features. The best test performances were obtained when considering EEG features in REM sleep only (Harrel's C-index: 0.723 ± 0.113; Uno's C-index: 0.741 ± 0.11; integrated Brier score: 0.174 ± 0.06). Features describing EEG slowing had high importance for the machine learning model. This is the first study employing machine learning applied to PSG to predict phenoconversion in patients with iRBD. If confirmed in larger cohorts, these findings might contribute to improving the design of clinical trials for neuroprotective treatments.

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke.

Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. Upper extremity robot therapy offers a number of advantages over manual therapies, but can suffer from slacking behavior, where the user lets the robot guide their movements even when they are capable of doing so by themselves, representing a major barrier to reaching the full potential of robot-assist rehabilitation. This is a pilot clinical study that investigates the use of an electromyography-based adaptive assist-as-needed controller to avoid slacking behavior during robotic rehabilitation for people with stroke. The study involved a convenience sample of five individuals with chronic stroke who underwent a robot therapy program utilizing horizontal arm tasks. The Fugl-Meyer assessment (FM) was used to document motor impairment status at baseline. Velocity, time, and position were quantified as performance parameters during the training. Arm and shoulder surface electromyography (EMG) and electroencephalography (EEG) were used to assess the controller's performance. The cross-sectional results showed strong second-order relationships between FM score and outcome measures, where performance metrics (path length and accuracy) were sensitive to change in participants with lower functional status. In comparison, speed, EMG and EEG metrics were more sensitive to change in participants with higher functional status. EEG signal amplitude increased when the robot suggested that the robot was inducing a challenge during the training tasks. This study highlights the importance of multi-sensor integration to monitor and improve upper-extremity robotic therapy.

Surface electromyography using dry polymeric electrodes.

Conventional wet Ag/AgCl electrodes are widely used in electrocardiography, electromyography (EMG), and electroencephalography (EEG) and are considered the gold standard for biopotential measurements. However, these electrodes require substantial skin preparation, are single use, and cannot be used for continuous monitoring (>24 h). For these reasons, dry electrodes are preferable during surface electromyography (sEMG) due to their convenience, durability, and longevity. Dry conductive elastomers (CEs) combine conductivity, flexibility, and stretchability. In this study, CEs combining poly(3,4-ehtylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) in polyurethane are explored as dry, skin contacting EMG electrodes. This study compares these CE electrodes to commercial wet Ag/AgCl electrodes in five subjects, classifying four movements: open hand, fist, wrist extension, and wrist flexion. Classification accuracy is tested using a backpropagation artificial neural network. The control Ag/AgCl electrodes have a 98.7% classification accuracy, while the dry conductive elastomer electrodes have a classification accuracy of 99.5%. As a conclusion, PEDOT based dry CEs were shown to successfully function as on-skin electrodes for EMG recording, matching the performance of Ag/AgCl electrodes, while addressing the need for minimal skin prep, no gel, and wearable technology.

AI-enabled Hybrid Model for Lower-Limb Movements Recognition using Estimated Muscular Activity from Cortical Brain Signals

Decades of research determined electromyography (EMG) and electroencephalography (EEG), individually, as paramount controls aimed at rehabilitation. However, correlating commands from the central nervous system to lower-limb movements are highly complex raising challenges for anthropomorphic lower-limb prosthesis control. This work establishes a hierarchical relationship between 12-channel EEG and 4-channel surface EMG using a hybrid model (LRG-2L-LSTM) for lower-limb ankle movement recognition by estimating muscular activity from cortical brain signals. The proposed model achieved R 2 = 0.742 ± 0.03, RMSE = 0.067 ± 0.002 for EMG estimation and averaged recognition accuracy of 84.86 ± 0.27% for ankle movements using estimated EMG, thereby, establishing lower-limb prosthesis and exoskeleton control for amputees with little to no muscular strength.

A Non-Newtonian liquid metal enabled enhanced electrography

Biopotential signals, like electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG), can help diagnose cardiological, musculoskeletal and neurological disorders. Dry silver/silver chloride (Ag/AgCl) electrodes are commonly used to obtain these signals. While a conductive hydrogel can be added to Ag/AgCl electrodes to improve the contact and adhesion between the electrode and the skin, dry electrodes are prone to movement. Considering that the conductive hydrogel dries over time, the use of these electrodes often creates an imbalanced skin-electrode impedance and a number of sensing issues in the front-end analogue circuit. This issue can be extended to several other electrode types that are commonly in use, in particular, for applications with a need for long-term wearable monitoring such as ambulatory epilepsy monitoring. Liquid metal alloys, such as eutectic gallium indium (EGaIn), can address key critical requirements around consistency and reliability but present challenges on low viscosity and the risk of leakage. To solve these problems, here, we demonstrate the use of a non-eutectic Ga–In alloy as a shear-thinning non-Newtonian fluid to offer superior performance to commercial hydrogel electrodes, dry electrodes, and conventional liquid metals for electrography measurements. This material has high viscosity when still and can flow like a liquid metal when sheared, preventing leakage while allowing the effective fabrication of electrodes. Moreover, the Ga–In alloy not only has good biocompatibility but also offers an outstanding skin-electrode interface, allowing for the long-term acquisition of high-quality biosignals. The presented Ga–In alloy is a superior alternative to conventional electrode materials for real-world electrography or bioimpedance measurement.

Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis

A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. The reliability of the application is highly dependent on an ability to decode intentions accurately based on captured neuromuscular signals. Physiological signals such as Electromyography (EMG) and Electroencephalography (EEG) generated by neuromuscular activities contain intrinsic patterns for users’ particular actions. Such actions can generally be classified as motor states, such as Forward, Reverse, Hand-Grip, and Hand-Release. To classify these motor states truthfully, the signals must be captured and decoded correctly. This paper proposes a novel classification technique using a Fuzzy Inference System (FIS) and a Long Short-Term Memory (LSTM) network to classify the motor states based on EMG signals. Existing EMG signal classification techniques generally rely on features derived from data captured at a specific time instance. This typical approach does not consider the temporal correlation of the signal in the entire window. This paper proposes an LSTM with a Fuzzy Logic method to classify four major hand movements: forward, reverse, raise, and lower. Features associated with the pattern generated throughout the motor state movement were extracted by exploring published data within a given time window. The classification results can achieve a 91.3% accuracy for the 4-way action (Forward/Reverse/GripUp/RelDown) and 95.1% (Forward/Reverse Action) and 96.7% (GripUp/RelDown action) for 2-way actions. The proposed mechanism demonstrates high-level, human-interpretable results that can be employed in rehabilitation or medical-device industries.

Dalgacık Aşırı Öğrenme Makinesi Otomatik Kodlayıcılarına Dayalı Öğretmenlerde EEG, EMG ve EKG Tabanlı Psikososyal Risk Düzeylerinin Belirlenmesi

Teachers who perform a sacred work are faced with many psychosocial risks. These risks can often be caused by the school administration, the students, and environmental factors. Machine learning and data mining approaches have recently gained much attention in social and educational researches. In this study, a novel approach, which is based on data augmentation and data classification, is proposed for the prediction of the psychosocial risk levels of the teachers. The data augmentation is carried out by using an extreme learning machine autoencoders (ELM-AE). More specifically, the wavelet activation function is incorporated into the ELM-AE to develop a novel approach called WELM-AE. After data augmentation, a traditional ELM classifier is used in the prediction of the psychosocial risk levels of teachers. A dataset, which contains physiological factors, namely Electrocardiography (ECG), Electromyography (EMG), and Electroencephalography (EEG), is used to evaluate the performance of the proposed method. Classification accuracy is used as the evaluation metric. All coding is carried out in MATLAB, and a 99.9% accuracy score is obtained with the proposed method. A performance comparison is also carried out with some machine learning techniques, namely decision trees (DT), support vector machines (SVM), and K-nearest neighbour (KNN). The results show that the proposed WELM-AE and ELM classifier outperform the compared methods.

Phase matters when there is power: Phasic modulation of corticospinal excitability occurs at high amplitude sensorimotor mu-oscillations

Prior studies have suggested that oscillatory activity in cortical networks can modulate stimulus-evoked responses through time-varying fluctuations in neural excitation-inhibition dynamics. Studies combining transcranial magnetic stimulation (TMS) with electromyography (EMG) and electroencephalography (EEG) can provide direct measurements to examine how instantaneous fluctuations in cortical oscillations contribute to variability in TMS-induced corticospinal responses. However, the results of these studies have been conflicting, as some reports showed consistent phase effects of sensorimotor mu-rhythms with increased excitability at the negative mu peaks, while others failed to replicate these findings or reported unspecific mu-phase effects across subjects. Given the lack of consistent results, we systematically examined the modulatory effects of instantaneous and pre-stimulus sensorimotor mu-rhythms on corticospinal responses with offline EEG-based motor evoked potential (MEP) classification analyses across five identical visits. Instantaneous sensorimotor mu-phase or pre-stimulus mu-power alone did not significantly modulate MEP responses. Instantaneous mu-power analyses showed weak effects with larger MEPs during high-power trials at the overall group level analyses, but this trend was not reproducible across visits. However, TMS delivered at the negative peak of high magnitude mu-oscillations generated the largest MEPs across all visits, with significant differences compared to other peak-phase combinations. High power effects on MEPs were only observed at the trough phase of ongoing mu oscillations originating from the stimulated region, indicating site and phase specificity, respectively. More importantly, such phase-dependent power effects on corticospinal excitability were reproducible across multiple visits. We provide further evidence that fluctuations in corticospinal excitability indexed by MEP amplitudes are partially driven by dynamic interactions between the magnitude and the phase of ongoing sensorimotor mu oscillations at the time of TMS, and suggest promising insights for (re)designing neuromodulatory TMS protocols targeted to specific cortical oscillatory states.

Transcranial magnetic stimulation as biomarker of excitability in drug development: A randomized, double-blind, placebo-controlled, cross-over study.

AimsThe purpose of this study was to investigate pharmacodynamic effects of drugs targeting cortical excitability using transcranial magnetic stimulation (TMS) combined with electromyography (EMG) and electroencephalography (EEG) in healthy subjects, to further develop TMS outcomes as biomarkers for proof‐of‐mechanism in early‐phase clinical drug development. Antiepileptic drugs presumably modulate cortical excitability. Therefore, we studied effects of levetiracetam, valproic acid and lorazepam on cortical excitability in a double‐blind, placebo‐controlled, 4‐way cross‐over study.MethodsIn 16 healthy male subjects, single‐ and paired‐pulse TMS‐EMG–EEG measurements were performed predose and 1.5, 7 and 24 hours postdose. Treatment effects on motor‐evoked potential, short and long intracortical inhibition and TMS‐evoked potential amplitudes, were analysed using a mixed model ANCOVA and cluster‐based permutation analysis.ResultsWe show that motor‐evoked potential amplitudes decreased after administration of levetiracetam (estimated difference [ED] −378.4 μV; 95%CI: −644.3, −112.5 μV; P < .01), valproic acid (ED −268.8 μV; 95%CI: −532.9, −4.6 μV; P = .047) and lorazepam (ED −330.7 μV; 95%CI: −595.6, −65.8 μV; P = .02) when compared with placebo. Long intracortical inhibition was enhanced by levetiracetam (ED −60.3%; 95%CI: −87.1%, −33.5%; P < .001) and lorazepam (ED −68.2%; 95%CI: −94.7%, −41.7%; P < .001) at a 50‐ms interstimulus interval. Levetiracetam increased TMS‐evoked potential component N45 (P = .004) in a central cluster and decreased N100 (P < .001) in a contralateral cluster.ConclusionThis study shows that levetiracetam, valproic acid and lorazepam decrease cortical excitability, which can be detected using TMS‐EMG–EEG in healthy subjects. These findings provide support for the use of TMS excitability measures as biomarkers to demonstrate pharmacodynamic effects of drugs that influence cortical excitability.

Emotion Recognition With Audio, Video, EEG, and EMG: A Dataset and Baseline Approaches

This paper describes a new posed multimodal emotional dataset and compares human emotion classification based on four different modalities - audio, video, electromyography (EMG), and electroencephalography (EEG). The results are reported with several baseline approaches using various feature extraction techniques and machine-learning algorithms. First, we collected a dataset from 11 human subjects expressing six basic emotions and one neutral emotion. We then extracted features from each modality using principal component analysis, autoencoder, convolution network, and mel-frequency cepstral coefficient (MFCC), some unique to individual modalities. A number of baseline models have been applied to compare the classification performance in emotion recognition, including k-nearest neighbors (KNN), support vector machines (SVM), random forest, multilayer perceptron (MLP), long short-term memory (LSTM) model, and convolutional neural network (CNN). Our results show that bootstrapping the biosensor signals (i.e., EMG and EEG) can greatly increase emotion classification performance by reducing noise. In contrast, the best classification results were obtained by a traditional KNN, whereas audio and image sequences of human emotions could be better classified using LSTM.

Is there a relationship between EEG and sTMS neurophysiological markers of the putative human mirror neuron system?

The mirror neuron system (MNS) has been theorized to play a neurobiological role in a number of social cognitive abilities and is commonly indexed putatively in humans via interpersonal motor resonance (IMR) and mu suppression. Although both indices are thought to measure similar neuronal populations (i.e., "mirror neurons"), it has been suggested that these methods are unrelated, and therefore, incompatible. However, prior studies reporting no relationships were typically conducted in small and underpowered samples. Thus, we aimed to investigate this potential association in a large sample of neurotypical adults (N=116; 72 females). Participants underwent transcranial magnetic stimulation (TMS), electromyography (EMG), and electroencephalography (EEG) during the observation of videos of actors performing grasping actions in order to index IMR and mu suppression (in beta, lower alpha, and upper alpha bandwidths). A series of linear regressions revealed no associations between IMR and each of the mu suppression bandwidths. Supplementary Bayesian analyses provided further evidence in favor of the null (B01 =8.85-8.93), providing further support for no association between the two indices of MNS activity. Our findings suggest that these two measures may indeed be unrelated indices that perhaps assess different neurophysiological aspects of the MNS. These results have important implications for future studies examining the MNS.

The bilateral limb deficit (BLD) phenomenon during leg press: a preliminary investigation into central and peripheral factors

BackgroundThe bilateral limb deficit (BLD) phenomenon suggests that lower forces are produced with bilateral limb contractions compared to the summed force produced when the same muscles are contracted unilaterally. While interhemispheric inhibition has been suggested as a cause of BLD, the origin of the deficit is yet to be determined. The aim of this study was to investigate central and peripheral factors responsible for the BLD during leg press using surface electromyography (EMG) and electroencephalography (EEG).MethodsFourteen adults (age = 23.7 ± 4.7 years old) completed bilateral (BL), unilateral left (UL) and unilateral right (UR) isometric leg press exercises. Bilateral limb ratio (BLR) was calculated similar to previous studies and surface EMG from three muscles of the quadriceps femoris (vastus lateralis, vastus medialis and rectus femoris) was used to measure the level of muscle activation. Movement related cortical potentials (MRCPs) over the left and right motor cortex areas (C3 and C4, respectively) were used to assess brain activity asymmetries reflecting central factors.ResultsNo significant difference was noted in the mean BLR (BLR = 94.8%), but a subset of ten participants did demonstrate a BLD (BLR = 81.4%, p < 0.01). Mean differences in relative activation were found among the three quadricep muscles (p < 0.001) with the right VM having significantly higher amplitude for the unilateral right (0.347 ± 0.318 mV) and bilateral right (0.436 ± 0.470 mV) conditions, respectively) than either the VL or RF (p < 0.05). The VL had significantly lower amplitudes in all conditions (0.127 ± 0.138 mV; 0.111 ± 0.104 mV; 0.120 ± 0.105 mV; 0.162 ± 0.147 mV for unilateral left, bilateral left, unilateral right, and bilateral right, respectively). However no overall significant differences were noted between bilateral and unilateral conditions. No significant differences in MRCPs were observed between brain activity of the C3 and C4 electrodes in any of the conditions.ConclusionWhile the sample size was low, this exploratory study noted the presence of BLD however the results did not provide evidence of significant limitations in either the EMG or EEG data.

Energy Efficient In-Memory Hyperdimensional Encoding for Spatio-Temporal Signal Processing

The emerging brain-inspired computing paradigm known as hyperdimensional computing (HDC) has been proven to provide a lightweight learning framework for various cognitive tasks compared to the widely used deep learning-based approaches. Spatio-temporal (ST) signal processing, which encompasses biosignals such as electromyography (EMG) and electroencephalography (EEG), is one family of applications that could benefit from an HDC-based learning framework. At the core of HDC lie manipulations and comparisons of large bit patterns, which are inherently ill-suited to conventional computing platforms based on the von-Neumann architecture. In this work, we propose an architecture for ST signal processing within the HDC framework using predominantly in-memory compute arrays. In particular, we introduce a methodology for the in-memory hyperdimensional encoding of ST data to be used together with an in-memory associative search module. We show that the in-memory HDC encoder for ST signals offers at least 1.80x energy efficiency gains, 3.36x area gains, as well as 9.74x throughput gains compared with a dedicated digital hardware implementation. At the same time it achieves a peak classification accuracy within 0.04% of that of the baseline HDC framework.

Dynamic Fusion of Electromyographic and Electroencephalographic Data towards Use in Robotic Prosthesis Control

We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as part of a brain-computer interface (BCI) the cost of EEG equipment commonly prevents this approach from being adopted outside the lab. This study demonstrates as a proof-of-concept that multimodal classification can be achieved by using low-cost EMG and EEG devices in tandem, with statistical decision-level fusion, to a high degree of accuracy. We present multiple fusion methods, including those based on Jensen-Shannon divergence which had not previously been applied to this problem. We report accuracies of up to 99% when merging both signal modalities, improving on the best-case single-mode classification. We hence demonstrate the strengths of combining EMG and EEG in a multimodal classification system that could in future be leveraged as an alternative control mechanism for robotic prostheses.

The effect of eye gaze direction on emotional mimicry: A multimodal study with electromyography and electroencephalography

Emotional mimicry plays an important role in social interaction and is influenced by social context, especially eye gaze direction. However, the neural mechanism underlying the effect of eye gaze direction on emotional mimicry is unclear. Here, we explored how eye gaze direction influenced emotional mimicry with a combination of electromyography (EMG) and electroencephalography (EEG) techniques, which may provide a more comprehensive measure. To do this, we recorded facial EMG and scalp EEG signals simultaneously while participants observed emotional faces (happy vs. angry) with direct or averted gaze. Then, we split the EEG trials into two mimicry intensity categories (high mimicry intensity, HMI vs. low mimicry intensity, LMI) according to EMG activity. The ERP difference between HMI and LMI EEG trials revealed four ERP components (P50, P150, N200 and P300), and the effect of eye gaze direction on emotional mimicry was prominent on P300 at P7 and P8. Moreover, we also observed differences in the effect of eye gaze direction on mimicry of happy faces and angry faces, which were found on P300 at P7, as well as P150 at P7 and N200 at P7 and Pz. In short, the present study isolated the neural signals of emotional mimicry with a new multimodal method, and provided empirical neural evidence that eye gaze direction affected emotional mimicry.

Design and Implementation of an EOG-based Mouse Cursor Control for Application in Human-Computer Interaction

Human Computer Interaction (HCI) has turned into an emerging technology due to the advancement in the field artificial intelligence and biomedical engineering. Acquiring different bio-signals such as Electro-oculography (EOG), Electromyography (EMG) and Electroencephalography (EEG) to control external machine or computer is the essence of HCI technology. In this research, we attempt to extract the EOG signal from different ways of eye movements and process it for HCI application. By utilizing Arduino, EOG data can be transmitted to computer and those signal characteristics is analysed through MATLAB. We have designed and implemented hardware and interfaced it with software to control a computer mouse cursor only by eye movement. Certain classification module like Support Vector machine (SVM) and Multilayer Perceptron (MLP) are used to classify different EOG data generated from different eye movement. According to the eye position, cursor automatically moves in that specific direction and PyAutoGUI module is used for this task. Results after experimentations with different subjects to control mouse cursor in real-time show that the average classification accuracy can reach up to 93% across all directions.

Information-based analysis of the relationship between brain and facial muscle activities in response to static visual stimuli.

Human facial muscles react differently to different visual stimuli. It is known that the human brain controls and regulates the activity of the muscles. In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain. Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content). Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals. This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.

Admittance Control of Powered Exoskeletons Based on Joint Torque Estimation

In recent years, exoskeletons have been widely accepted as rehabilitative and walking assistive devices for either healthy persons or patients with mobility impairment. Depending on mobility of the wearers, the control strategies for exoskeletons may differ significantly. This paper aims at developing an admittance control framework for exoskeletons to assist healthy persons in safer, faster, or more energy-efficient walking. A crucial step to accomplish assistive walking for healthy persons is to detect their intention. Direct sensing of the wearer's biological signals, such as electromyography (EMG) and electroencephalography (EEG), requires additional sensors, which increases the cost and makes it inconvenient for the wearer to put on and take off the exoskeleton. Instead, we propose to detect the wearer's intention by estimating the total torques applied from the wearer to the human-exoskeleton system based on the motor current and joint angles. Then the velocity commands to the joint motors are generated according to the estimated torque and a predefined mechanical admittance function of each joint. Consequently, the exoskeleton complies with the wearer's intention. Rigorous theoretical analysis is performed on robust stability of the closed-loop system. Then experiments are carried out to verify the accuracy and robustness of the proposed torque estimation algorithm. In addition, experimental data show that the wearer's gait can be shaped in a desired way by choosing an appropriate admittance function in the proposed admittance control loop.

A −68 dB THD, 0.6 mm2 Active Area Biosignal Acquisition System With a 40–320 Hz Duty-Cycle Controlled Filter

This paper presents a reconfigurable front-end (FE) circuit for acquiring various low-frequency biomedical signals. An energy and area-efficient tunable filter is proposed for adapting the FE bandwidth to the signal of interest. The filter is designed using a switched-R-MOSFET-C (SRMC) technique to realize the needed ultra-low cutoff frequency. An 8-bit SAR ADC, following the filter, quantizes the signal, while the SAR control logic is re-used to accurately program the filter bandwidth from 40 Hz to 320 Hz with a 40 Hz step. The prototype chip includes the complete FE system, formed of an instrumentation amplifier (IA), a programmable-gain amplifier (PGA), and the proposed tunable filter followed by the SAR ADC. Implemented in 0.13 $\mu \text{m}$ CMOS technology, the IC occupies a 0.6 mm2 active area while consuming 6.3 $\mu \text{W}$ dc power from a 2-V supply. Measurement results show a FE gain range of 43–55 dB with an integrated input-referred noise ( ${V_{\text {IRN}}}$ ) of 3.45 $\mu V_{\text {rms}}$ , a 66 dB dynamic range (DR), and a total-harmonic distortion (THD) of −68 dB at an input amplitude of 6 $\text{m}V_{PP}$ . The effective number of bits (ENOB) for the ADC is 7.921 bits at 1-kS/s. In real-time Electrocardiogram (ECG), Electromyography (EMG), and Electroencephalography (EEG) measurements, high-fidelity waveforms are acquired using the proposed FE IC, validating the system’s reconfigurability and high-linearity.

A 12.6 mW, 573–2901 kS/s Reconfigurable Processor for Reconstruction of Compressively Sensed Physiological Signals

This article presents a reconfigurable processor based on the alternating direction method of multipliers (ADMM) algorithm for reconstructing compressively sensed physiological signals. The architecture is flexible to support physiological ExG [electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG)] signal with various signal dimensions (128, 256, 384, and 512). Data characteristics are utilized to substantially reduce the overall hardware complexity by up to 99%. A 16 $\times $ folded architecture achieves a 64% area-power product reduction compared with the unfolded one. A customized buffer is used for multi-word access, which reduces data latency by four times. It dissipates 75% less power with only 25% area when compared with the realization with conventional flip-flops. As a proof of concept, a reconfigurable processor for reconstructing ExG signals is presented. Fabricated in a 40-nm CMOS technology, the processor integrates 3.69-M gates in 3.23 mm2. The chip delivers a throughput of 573–2901 kSamples/s (kS/s) for ExG signals and dissipates less than 12.6 mW at 87 MHz from a 0.60-V supply. Compared with state-of-the-art designs, the chip achieves a 1.5-to-14 $\times $ higher throughput with 3.2-to-11 $\times $ less energy, given the performance specification [reconstruction signal-to-noise ratio (RSNR) ≥ 15 dB].