Localized Electrical Impedance Myography of the Biceps Brachii Muscle during Different Levels of Isometric Contraction and Fatigue

Past, present, and future of electrical impedance tomography and myography for medical applications: a scoping review

This scoping review summarizes two emerging electrical impedance technologies: electrical impedance myography (EIM) and electrical impedance tomography (EIT). These methods involve injecting a current into tissue and recording the response at different frequencies to understand tissue properties. The review discusses basic methods and trends, particularly the use of electrodes: EIM uses electrodes for either injection or recording, while EIT uses them for both. Ag/AgCl electrodes are prevalent, and current injection is preferred over voltage injection due to better resistance to electrode wear and impedance changes. Advances in digital processing and integrated circuits have shifted EIM and EIT toward digital acquisition, using voltage-controlled current sources (VCCSs) that support multiple frequencies. The review details powerful processing algorithms and reconstruction tools for EIT and EIM, examining their strengths and weaknesses. It also summarizes commercial devices and clinical applications: EIT is effective for detecting cancerous tissue and monitoring pulmonary issues, while EIM is used for neuromuscular disease detection and monitoring. The role of machine learning and deep learning in advancing diagnosis, treatment planning, and monitoring is highlighted. This review provides a roadmap for researchers on device evolution, algorithms, reconstruction tools, and datasets, offering clinicians and researchers information on commercial devices and clinical studies for effective use and innovative research.

A comparison of electromyography techniques: surface versus intramuscular recording

This review is a comprehensive guide for electromyography (EMG) researchers, providing a comparison of skin EMG recording (surface EMG: sEMG and high-density sEMG: HD-sEMG) and intramuscular EMG recording (multi-motor unit-MMU and single motor unit electromyography-SMU). We delve into the nuances of techniques, highlighting their strengths and limitations in quantifying muscle activation during dynamic and static conditions. We first examine how EMG signals change with time, focussing on the interplay between motor unit synchronisation and signal amplitude. The review then explores the impact of electrode placement on signal quality. We further discuss the challenges of signal cancellation, crosstalk from neighbouring muscles, and motion artifacts, which can significantly affect signal integrity. Finally, we address the temporal changes in electrode impedance and its implications for data interpretation. Our analysis proposes that specific research objectives should guide the choice amongst sEMG, HD-sEMG, SMU and MMU. MMU, which records the peak counts of individual motor unit action potentials from a localised muscle area, is particularly suited for studying deep or small muscles during static and dynamic activities. Its high sensitivity to motor unit recruitment and discharge rates minimises the impact of factors such as signal cancellation and motion artefacts. Conversely, sEMG is well-suited for short-duration, isometric assessments of large, superficial muscles. HD-sEMG helps study single motor unit properties under isometric conditions. SMU is particularly suited for studying neuronal networks between stimulated sites and motor neurons. This review aims to provide researchers with the information to select the most appropriate EMG technique for their investigations.

Fascicle Behavior and Muscle Activity of The Biceps Femoris Long Head during Running at Increasing Speeds

Hamstring strain injuries (HSIs) are prevalent in sports involving high-speed running and most of the HSIs are biceps femoris long head (BFlh) injuries. The primary cause for HSIs during high-speed running remains controversial due to the lack of in vivo measurement of the BFlh muscle behavior during running. Therefore, the purpose of this study was to quantify the muscle-tendon unit (MTU) and fascicle behavior of BFlh during running. Seven college male sprinters (22.14 ± 1.8 years; 177.7 ± 2.5 cm; 70.57 ± 5.1 kg; personal bests in 100m: 11.1 ± 0.2 s) were tested on a motorized treadmill instrumented with two force plate for running at 4, 5, 6m/s. The ground reaction force (GRF), 3D lower limb kinematics, EMG, and ultrasound images of biceps femoris long head (BFlh) in the middle region were recorded simultaneously. BFlh fascicles undergo little length change (about 1 cm) in the late swing phase during running at three submaximal speeds. BFlh fascicle lengthening accounted for about 30% of MTU length change during the late swing phase. BFlh was most active during the late swing and early stance phases, ranging from 83%MVC at a running speed of 4 m/s to 116%MVC at 6 m/s. Muscle fascicles in the middle region of BFlh undergo relatively little lengthening relative to the MTU in the late swing phase during running in comparison to results from simulation studies. These results suggest that there is a decoupling between the fascicle in the middle region and MTU length changes during the late swing phase of running.

Association between muscle-localized bioelectrical impedance analysis parameters and performance in a multi-set exercise on the isokinetic dynamometer in young women

This study aimed to verify the relationship between changes in thigh muscle-localized bioelectrical impedance analysis (ML-BIA) parameters and performance in a multiple-set exercise. The sample consisted of 30 female university students (22.1 ± 3.2 years). The ML-BIA parameters, including localized muscle resistance (ML-R), reactance (ML-Xc), and phase angle (ML-AngF), were evaluated using a tetrapolar bioelectric impedance device operating at a frequency of 50 KHz. The multiple sets protocol was performed with an isokinetic dynamometer. For body composition, total and leg lean soft tissue (LST) were evaluated using dual X-ray absortiometry. Student's t-test for paired samples was used to compare the ML-BIA parameters and thigh circumference pre and postexercise. Linear regression analysis was performed to verify the ∆ML-PhA as a predictor of peak torque for the three sets alone while controlling for total and leg LST. There were differences in the ML-R (∆ = 0.02 ± 1.45 Ω; p = 0.001; and E.S = 0.19), ML-Xc (∆ = 2.90 ± 4.12 Ω; p = 0.043; and E.S = 0.36), and thigh circumference (∆ = 0.82 ± 0.60 cm; p < 0.001; and E.S = 0.16) pre- and post-multiple sets. ΔML-PhA was a predictor of performance in the first set (p = 0.002), regardless of total and leg LST. However, the ΔML-PhA lost its explanatory power in the other sets (second and third), and the variables that best explained performance were total and leg LST. The ML-BIA (ML-R and ML-Xc) parameters were sensitive and changed after the multiple sets protocol, and the ΔML-PhA was a predictor of performance in the first set regardless of the total and leg LST.

Circuital modelling in muscle tissue impedance measurements

Electrical impedance spectroscopy (EIS) stands as a widely employed characterization technique for studying muscular tissue in both physio/pathological conditions. This methodology commonly involves modeling tissues through equivalent electrical circuits, facilitating a correlation between electrical parameters and physiological properties. Within existing literature, diverse equivalent electrical circuits have been proposed, varying in complexity and fitting properties. However, to date, none have definitively proven to be the most suiTable for tissue impedance measurements. This study aims to outline a systematic methodology for EIS measurements and to compare the performances of three widely used electrical circuits in characterizing both physiological and pathological muscle tissue conditions. Results highlight that, for optimal fitting with electrical parameters relevant to tissue characterization, the choice of the circuit to be fitted closely hinges on the specific measurement objectives, including measurement parameters and associated physiological features. Naturally, this necessitates a balance between simplicity and fitting accuracy.

Is muscle localized phase angle an indicator of muscle power and strength in young women?

Objective. This study aimed to investigate the capacity of the bioelectrical muscle localized phase angle (ML-PhA) as an indicator of muscle power and strength compared to whole body PhA (WB-PhA).Approach. This study assessed 30 young women (22.1 ± 3.2 years) for muscle power and strength using the Wingate test and isokinetic dynamometer, respectively. Bioimpedance analysis at 50 kHz was employed to assess WB-PhA and ML-PhA. Lean soft tissue (LST) and fat mass (FM) were quantified using dual x-ray absorptiometry. Performance values were stratified into tertiles for comparisons. Regression and mediation analysis were used to test WB-PhA and ML-PhA as performance predictors.Main results. Women in the second tertile of maximum muscle power demonstrated higher ML-PhA values than those in first tertile (13.6° ± 1.5° versus 11.5° ± 1.5°,p= 0.031). WB-PhA was a predictor of maximum muscle power even after adjusting for LST and FM (β= 0.40,p= 0.039). ML-PhA alone predicted average muscle power (β= 0.47,p= 0.008). FM percentage was negatively related to ML-PhA and average muscle power, and it mediated their relationship (b= 0.14; bias-corrected and accelerated 95% confidence interval: 0.007-0.269).Significance. PhA values among tertiles demonstrated no differences and no correlation for strength variables. The results revealed that both WB and ML-PhA may be markers of muscle power in active young women.

Electrical impedance myography combined with quantitative assessment techniques in paretic muscle of stroke survivors: Insights and challenges

Aging is a non-modifiable risk factor for stroke and the global burden of stroke is continuing to increase due to the aging society. Muscle dysfunction, common sequela of stroke, has long been of research interests. Therefore, how to accurately assess muscle function is particularly important. Electrical impedance myography (EIM) has proven to be feasible to assess muscle impairment in patients with stroke in terms of micro structures, such as muscle membrane integrity, extracellular and intracellular fluids. However, EIM alone is not sufficient to assess muscle function comprehensively given the complex contributors to paretic muscle after an insult. This article discusses the potential to combine EIM and other common quantitative methods as ways to improve the assessment of muscle function in stroke survivors. Clinically, these combined assessments provide not only a distinct advantage for greater accuracy of muscle assessment through cross-validation, but also the physiological explanation on muscle dysfunction at the micro level. Different combinations of assessments are discussed with insights for different purposes. The assessments of morphological, mechanical and contractile properties combined with EIM are focused since changes in muscle structures, tone and strength directly reflect the muscle function of stroke survivors. With advances in computational technology, finite element model and machine learning model that incorporate multi-modal evaluation parameters to enable the establishment of predictive or diagnostic model will be the next step forward to assess muscle function for individual with stroke.

Immediate Effects of Functional Electrical Stimulation-Assisted Cycling on the Paretic Muscles of Patients With Hemiparesis After Stroke: Evidence From Electrical Impedance Myography

Background

Electrical impedance myography (EIM) has been applied to assess muscle health conditions in neuromuscular disorders. This study aimed to detect immediate muscle electrical impedance property alterations in lower extremity of chronic stroke survivors immediately after functional electrical stimulation (FES)-assisted cycling training.

Methods

Fourteen chronic stroke survivors were recruited for the current study. EIM measurements were conducted before and immediately after 40-min FES-assisted cycling training for each subject. Four interested muscle groups [rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and the medial head of gastrocnemius (MG)] were selected. Correlation analysis was performed to reveal a significant correlation between changes in EIM parameters and clinical scales [Fugl–Meyer Assessment of the lower extremity (FMA-LE); 6-min walking test (6MWT)].

Results

Immediately after training, reactance (X) and phase angle (θ) values significantly increased on the TA and MG muscles. Significant correlation was observed between X value and FMA-LE scores (r = 0.649, p = 0.012) at MG as well as X and FMA scores of the ankle joint (r = 0.612, p = 0.02). Resistance (R) and θ were significantly correlated with 6MWT score (R-6MWT: r = 0.651, p = 0.012; θ-6MWT: r = 0.621, p = 0.018).

Conclusion

This brief report demonstrated that EIM can reveal the intrinsic property alteration in the paretic muscle of chronic stroke survivors immediately after FES-assisted cycling training. These alterations might be related to muscle hypertrophy (i.e., increases in muscle fiber size). This brief report might aid the understanding of the mechanism of electrical stimulation-assisted exercise in improving muscle function of stroke survivors.

SeNic: An Open Source Dataset for sEMG-Based Gesture Recognition in Non-ideal Conditions

In order to reduce the gap between the laboratory environment and actual use in daily life of human-machine interaction based on surface electromyogram (sEMG) intent recognition, this paper presents a benchmark dataset of sEMG in non-ideal conditions (SeNic). The dataset mainly consists of 8-channel sEMG signals, and electrode shifts from an 3D-printed annular ruler. A total of 36 subjects participate in our data acquisition experiments of 7 gestures in non-ideal conditions, where non-ideal factors of 1) electrode shifts, 2) individual difference, 3) muscle fatigue, 4) inter-day difference, and 5) arm postures are elaborately involved. Signals of sEMG are validated first in temporal and frequency domains. Results of recognizing gestures in ideal conditions indicate the high quality of the dataset. Adverse impacts in non-ideal conditions are further revealed in the amplitudes of these data and recognition accuracies. To be concluded, SeNic is a benchmark dataset that introduces several non-ideal factors which often degrade the robustness of sEMG-based systems. It could be used as a freely available dataset and a common platform for researchers in the sEMG-based recognition community. The benchmark dataset SeNic are available online via the website3.

System Performance and User Feedback Regarding Wearable Bioimpedance System for Multi-Site Knee Tissue Monitoring: Free-Living Pilot Study With Healthy Adults

Knee-focused wearable devices have the potential to support personalized rehabilitation therapies by monitoring localized tissue alterations related to activities that reduce functional symptoms and pain. However, supporting these applications requires reported data to be reliable and accurate which can be challenging in the unsupervised free-living conditions that wearable devices are deployed. This pilot study has assessed a knee-focused wearable sensor system to quantify 1) system performance (operation, rates of data artifacts, environment impacts) to estimate realistic targets for reliable data with this system and 2) user experiences (comfort, fit, usability) to help inform future designs to increase usability and adoption of knee-focused wearables. Study data was collected from five healthy adult participants over 2 days, with 84.5 and 35.9% of artifact free data for longitudinal and transverse electrode configurations. Small to moderate positive correlations were also identified between changes in resistance, temperature, and humidity with respect to acceleration to highlight how this system can be used to explore relationships between knee tissues and environmental/activity context.

Bioelectrical Impedance Vector and Creatine Phosphokinase Changes Induced by a High-Intensity Training Session in Rink Hockey Players

This study aimed to analyze anthropometric and whole-body/muscle-localized bioelectrical impedance vector analysis (BIVA) adaptations and their relation to creatine kinase (CK) as a biomarker of muscle damage in a group of seven male players in the maximum category of professional rink hockey. There were three checkpoint assessments in relation to a high-intensity training session: pre-session (PRE), post-session (POST), and 24 h-post-session (POST24H). The resistance, reactance, and impedance module were adjusted by height (R/h, Xc/h, and Z/h, respectively). The Wilcoxon signed-rank test was used to compare the data at baseline and follow-up, while Spearman correlation was used to explore the relationship between CK and the rest of the parameters. The results registered a decrease in body mass at POST (p = 0.03) and a reestablishment at POST24H (p = 0.02). Whole-body BIVA registered a significant increase in R/h between PRE–to–POST (p = 0.02) and returned to baseline values at POST24H (p = 0.02), which was expected since this parameter is related to hydration processes. Muscle-localized BIVA in the rectus femoris muscle showed an increase in both Xc/h and phase angle in POST (p = 0.04 and p = 0.03, respectively) and a decrease in Xc/h at POST24H (p = 0.02). CK correlated with R/h in the rectus femoris at all the checkpoints (PRE–to–POST: r = 0.75, p = 0.05; PRE–to–POST24H: r = 0.81, p = 0.03; POST–to–POST24H: r = 0.82, p = 0.02). Our results indicate that BIVA is a sensitive methodology to assess general and muscle-localized hydration induced by a high-intensity training session in rink hockey players. A correlation between BIVA and CK was also reported.

Threshold and Trend Artifacts in Localized Multi-Frequency Bioimpedance Measurements

Localized tissue bioimpedance is being widely investigated as a technique to identify physiological features in support of health focused applications. In support of this method being translated into wearable systems for continuous monitoring, it is critical to not only collect measurements but also evaluate their quality. This is necessary to reduce errors in equipment or measurement conditions from contributing data artifacts to datasets that will be analyzed. Two methods for artifact identification in resistance measurements of bioimpedance datasets are presented. These methods, based on thresholding and trend detection, are applied to localized knee bioimpedance datasets collected from two knee sites over 7 consecutive days in free-living conditions. Threshold artifacts were identified in 0.04% (longitudinal and transverse) and 0.69% (longitudinal) /3.50% (transverse) of the total data collected.

Electrical Impedance Myography in Health and Physical Exercise: A Systematic Review and Future Perspectives

Background: Electrical impedance myography (EIM) is a non-invasive method that provides information about muscle health and changes that occur within it. EIM is based on the analysis of three impedance variables: resistance, reactance, and the phase angle. This systematic review of the literature provides a deeper insight into the scope and range of applications of EIM in health and physical exercise. The main goal of this work was to systematically review the studies on the applications of EIM in health and physical exercise in order to summarize the current knowledge on this method and outline future perspectives in this growing area, including a proposal for a research agenda. Furthermore, some basic assessment principles are provided. Methods: Systematic literature searches on PubMed, Scopus, SPORTDiscus and Web of Science up to September 2020 were conducted on any empirical investigations using localized bioimpedance devices to perform EIM within health and physical exercise contexts. The search included healthy individuals, elite soccer players with skeletal muscle injury, and subjects with primary sarcopenia. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist was used to develop the systematic review protocol. The quality and risk of bias of the studies included were assessed with the AQUA tool. Results: Nineteen eligible original articles were included in this review, which were separated into three tables according to the nature of the study. The first table includes six studies on the bioelectrical characterization of muscle. The second table includes five studies analyzing muscle changes in injured elite soccer players. The third table includes studies on the short-, medium-, and long-term bioelectrical adaptations to physical exercise. Conclusions: EIM has been used for the evaluation of the muscle condition in the clinical field over the last few years, especially in different neuromuscular diseases. It can also play an important role in other contexts as an alternative to complex and expensive methods such as magnetic resonance imaging. However, further research is needed. The main step in establishing EIM as a valid tool in the scientific field is to standardize the protocol for performing impedance assessments.

Cole-impedance parameters representing biceps tissue bioimpedance in healthy adults and their alterations following eccentric exercise

The purpose of this study is to identify if participation in an eccentric exercise protocol altered the Cole-impedance model parameters that represent localized bicep tissue bioimpedance. This supports continued efforts to identify which features of tissue bioimpedance may be effective markers to non-invasively identify skeletal muscle damage. Here, the Cole-impedance model parameters that best fit the localized electrical impedance of exercised (using an eccentric stimulus) and unexercised biceps of 6 participants (collected before, immediately after and at 24 h, 48 h, 72 h and 96 h) are determined using a numerical optimization technique. Statistical tests comparing the pre-exercise and post-exercise model parameters report significant decreases in R ∞ and R 1 with significant increases in C at 72 h and 96 h post-exercise for exercised biceps (aligning with noted periods of peak swelling). These changes in R ∞ , R 1 , and C were not observed in the unexercised biceps. These results support that the C parameter of the Cole-impedance model fit to bioimpedance data may be a suitable marker for identifying skeletal muscle damage.

Spasticity Assessment Based on the Maximum Isometrics Voluntary Contraction of Upper Limb Muscles in Post-stroke Hemiplegia

Background: The assessment of muscle properties is an essential prerequisite in the treatment of post-stroke patients with limb spasticity. Most existing spasticity assessment approaches do not consider the muscle activation with voluntary contraction. Including voluntary movements of spastic muscles may provide a new way for the reliable assessment of muscle spasticity.

Objective: In this study, we investigated the effectiveness and reliability of maximum isometrics voluntary contraction (MIVC) based method for spasticity assessment in post-stroke hemiplegia.

Methods: Fourteen post-stroke hemiplegic patients with arm spasticity were asked to perform two tasks: MIVC and passive isokinetic movements. Three biomechanical signals, torque, position, and time, were recorded from the impaired and non-impaired arms of the patients. Three features, peak torque, keep time of the peak torque, and rise time, were computed from the recorded MIVC signals and used to evaluate the muscle voluntary activation characteristics, respectively. For passive movements, two features, the maximum resistance torque and muscle stiffness, were also obtained to characterize the properties of spastic stretch reflexes. Subsequently, the effectiveness and reliability of the MIVC-based spasticity assessment method were evaluated with spearman correlation analysis and intra class correlation coefficients (ICCs) metrics.

Results: The results indicated that the keep time of peak torque and rise time in the impaired arm were higher in comparison to those in the contralateral arm, whereas the peak torque in the impaired side was significantly lower than their contralateral arm. Our results also showed a significant positive correlation (r = 0.503, p = 0.047) between the keep time (t_k) and the passive resistant torque. Furthermore, a significantly positive correlation was observed between the keep time (tk) and the muscle stiffness (r = 0.653, p = 0.011). Meanwhile, the ICCs for intra-time measurements of MIVC ranged between 0.815 and 0.988 with one outlier.

Conclusion: The findings from this study suggested that the proposed MIVC-based approach would be promising for the reliable and accurate assessment of spasticity in post-stroke patients.

Time-course bicep tissue bio-impedance changes throughout a fatiguing exercise protocol

This study investigated the localized electrical-impedance changes in the biceps tissues throughout a fatiguing exercise protocol. During the protocol, 17 subjects performed 10 sets of bicep curl repetitions at either 60% or 75% of their one-repetition maximum weight until task failure. The localized tissue impedance (resistance, reactance, phase angle) was measured at 10 kHz, 50 kHz, and 100 kHz immediately after each of 10 sets for comparison against the baseline pre-fatigue measures. A trend of decreasing resistance and reactance magnitude were observed, with greater changes occurring as the protocol progressed. Statistical testing demonstrated statistically significant changes in resistance, reactance, and phase angle for both groups of participants. The significant changes in resistance were observed at earlier time-points than the reactance and phase angle changes for both groups.

Design and Evaluation of an Electrical Bioimpedance Device Based on DIBS for Myography during Isotonic Exercises

Electrical Bioimpedance Spectroscopy (EIS) is a technique used to assess passive electrical properties of biological materials. EIS detects physiological and pathological conditions in animal tissues. Recently, the introduction of broadband excitation signals has reduced the measuring time for application techniques such as Electrical Bioimpedance Myography. Therefore, this work is aimed at proposing a prototype by using discrete interval binary sequences (DIBS), which is based on a system that holds a current source, impedance acquisition system, microcontroller and graphical user interface. Measurements between 5 Ω to 5 kΩ had impedance acquisition and phase angle errors of aproximately 2% and were lower than 3 degrees, respectively. Based on a proposed circuit, bioimpedance of the chest muscle (Pectoralis Major) was measured during isotonic exercise (push-up). As a result, our analyses have detected tiredness and fatigue. We have explored and proposed new parameters which assess such conditions, as both the maximum magnitude and tiredness coefficient. These parameters decrease exponentially with consecutive push-ups and were convergent in the majority of the sixteen days of measurement.

Fatigue-Induced Cole Electrical Impedance Model Changes of Biceps Tissue Bioimpedance

Bioimpedance, or the electrical impedance of biological tissues, describes the passive electrical properties of these materials. To simplify bioimpedance datasets, fractional-order equivalent circuit presentations are often used, with the Cole-impedance model being one of the most widely used fractional-order circuits for this purpose. In this work, bioimpedance measurements from 10 kHz to 100 kHz were collected from participants biceps tissues immediately prior and immediately post completion of a fatiguing exercise protocol. The Cole-impedance parameters that best fit these datasets were determined using numerical optimization procedures, with relative errors of within approximately ± 0.5 % and ± 2 % for the simulated resistance and reactance compared to the experimental data. Comparison between the pre and post fatigue Cole-impedance parameters shows that the R ∞ , R 1 , and f p components exhibited statistically significant mean differences as a result of the fatigue induced changes in the study participants.

Inherent physiological artifacts in EEG during tDCS

Online imaging and neuromodulation is invalid if stimulation distorts measurements beyond the point of accurate measurement. In theory, combining transcranial Direct Current Stimulation (tDCS) with electroencephalography (EEG) is compelling, as both use non-invasive electrodes and image-guided dose can be informed by the reciprocity principle. To distinguish real changes in EEG from stimulation artifacts, prior studies applied conventional signal processing techniques (e.g. high-pass filtering, ICA). Here, we address the assumptions underlying the suitability of these approaches. We distinguish physiological artifacts - defined as artifacts resulting from interactions between the stimulation induced voltage and the body and so inherent regardless of tDCS or EEG hardware performance - from methodology-related artifacts - arising from non-ideal experimental conditions or non-ideal stimulation and recording equipment performance. Critically, we identify inherent physiological artifacts which are present in all online EEG-tDCS: 1) cardiac distortion and 2) ocular motor distortion. In conjunction, non-inherent physiological artifacts which can be minimized in most experimental conditions include: 1) motion and 2) myogenic distortion. Artifact dynamics were analyzed for varying stimulation parameters (montage, polarity, current) and stimulation hardware. Together with concurrent physiological monitoring (ECG, respiration, ocular, EMG, head motion), and current flow modeling, each physiological artifact was explained by biological source-specific body impedance changes, leading to incremental changes in scalp DC voltage that are significantly larger than real neural signals. Because these artifacts modulate the DC voltage and scale with applied current, they are dose specific such that their contamination cannot be accounted for by conventional experimental controls (e.g. differing stimulation montage or current as a control). Moreover, because the EEG artifacts introduced by physiologic processes during tDCS are high dimensional (as indicated by Generalized Singular Value Decomposition- GSVD), non-stationary, and overlap highly with neurogenic frequencies, these artifacts cannot be easily removed with conventional signal processing techniques. Spatial filtering techniques (GSVD) suggest that the removal of physiological artifacts would significantly degrade signal integrity. Physiological artifacts, as defined here, would emerge only during tDCS, thus processing techniques typically applied to EEG in the absence of tDCS would not be suitable for artifact removal during tDCS. All concurrent EEG-tDCS must account for physiological artifacts that are a) present regardless of equipment used, and b) broadband and confound a broad range of experiments (e.g. oscillatory activity and event related potentials). Removal of these artifacts requires the recognition of their non-stationary, physiology-specific dynamics, and individualized nature. We present a broad taxonomy of artifacts (non/stimulation related), and suggest possible approaches and challenges to denoising online EEG-tDCS stimulation artifacts.

Estimating Localized Bio-impedance with Measures from Multiple Redundant Electrode Configurations

In most bio-impedance applications, measurements are collected from a single electrode configuration though multi-electrode systems could monitor different tissue sites or serve as a source of redundancy in case of electrode malfunction. However, comparison of impedance data collected from different electrode configurations is difficult. This article proposes an approach to estimate the current tissue impedance collected from a fixed electrode configuration using measurements from different sites of the same localized tissue. Estimated impedance is calculated using the ratio between impedance values from different electrode configurations collected prior to the electrode malfunction event. This approach is validated using measurements of a human forearm collected from 3 kHz to 1 MHz collected with 4 electrode configurations over 3 days. The estimation results using this approach show maximum estimation errors of 2.3% and 16.9% for the resistance and reactance, respectively, compared to the measured impedance.

An investigation of the accuracy and reliability of body composition assessed with a handheld electrical impedance myography device

The purpose of this study was to compare the body fat per cent (BF%) assessed with a unique handheld electrical impedance myography (EIM) device, along with other popular methods, to dual-energy X-ray absorptiometry (DXA). Participants included 33 males (aged 24.3 ± 4.6 years) and 38 females (aged 25.3 ± 8.9 years) who completed 2 visits separated by 24-72 h. The assessments included DXA, bioelectrical impedance analysis (BIA), skinfold measures (SKF), and three separate EIM measurements. No significant differences in BF% (P > 0.05) were found between all EIM assessments when compared against DXA for both males and females for each visit. All methods showed no significant differences in BF% (P > 0.05) between days within themselves. Across both days, the standard error of the estimate (SEE) for the EIM measurements ranged from 2.66% to 3.15%, the SEE for BIA was 2.80 and 2.85, and for SKF was 2.90 and 2.82. The 95% limits of agreement ranged from ±5.34% to ±6.38% for EIM measurements and were highest for SKF (±7.42% and ±7.47%). The total error for both days was largest for SKF (5.20% and 5.35%) and lowest for the EIM measurements (2.48-3.24%). This investigation supports use of a handheld EIM device as an accurate and reliable method of estimating BF% compared to DXA in young, apparently healthy individuals with BF% in the range of 10-22% for males and 20-32% in females and suggests this EIM device be considered a viable alternative to other established field measurements in this population.

A Multitasking Electrical Impedance Tomography System Using Titanium Alloy Electrode

This paper presents a multitasking electrical impedance tomography (EIT) system designed to improve the flexibility and durability of an existing EIT system. The ability of the present EIT system to detect, locate, and reshape objects was evaluated by four different experiments. The results of the study show that the system can detect and locate an object with a diameter as small as 1.5 mm in a testing tank with a diameter of 134 mm. Moreover, the results demonstrate the ability of the current system to reconstruct an image of several dielectric object shapes. Based on the results of the experiments, the programmable EIT system can adapt the EIT system for different applications without the need to implement a new EIT system, which may help to save time and cost. The setup for all the experiments consisted of a testing tank with an attached 16-electrode array made of titanium alloy grade 2. The titanium alloy electrode was used to enhance EIT system's durability and lifespan.