Use of the integrated profile for voluntary muscle activity detection using EMG signals with spurious background spikes: A study with incomplete spinal cord injury

Decisional components of motor responses are not related to online response control: Evidence from lexical decision and speed-accuracy tradeoff manipulations

Evidence suggests that decision processes can propagate to motor-response execution. However, the functional characterization of motor decisional components is not yet fully understood. By combining a classic lexical decision experiment with manipulations of speed-accuracy tradeoff (SAT), the present experiment assessed the hypothesis that decisional effects on chronometric measures of motor-response execution are related to online response control. The electromyographic (EMG) signal associated with manual button-press responses was used to dissociate the premotor component (from stimulus onset until the onset of the EMG activity) from the motor component (from EMG onset until the button-press), thus enabling the assessment of decision-related effects in terms of motor-response duration within single-trial reaction times. Other than replicating all the previously reported SAT effects, the experiment revealed hindered control processes when the instructions emphasized speed over accuracy, as indicated by measures of response control such as partial errors, fast errors, and correction likelihood. Nonetheless, the lexicality effect on motor responses, consisting of slower motor times for pseudowords compared to words, was impervious to any SAT modulation. The results suggest that SAT-induced variations in decision and response control policies may not be the prominent determinant of decision-related effects on motor times, highlighting the multiple "cognitive" components that affect peripheral response execution.

Temporal unpredictability increases error monitoring as revealed by EEG-EMG investigation

Reacting in an unpredictable context increases error monitoring as evidenced by greater error-related negativity (ERN), an electrophysiological marker linked to an evaluation of response outcomes. We investigated whether ERN also increased when participants evaluated their responses to events that appeared in unpredictable versus predictable moments in time. We complemented electroencephalographic (EEG) analysis of cortical activity by measuring performance monitoring processes at the peripheral level using electromyography (EMG). Specifically, we used EMG data to quantify how temporal unpredictability would affect motor time (MT), the interval between the onset of muscle activity, and the mechanical response. MT increases following errors, indexing online error detection, and an attempt to stop incorrect actions. In our temporally cued version of the stop-signal task, symbolic cues predicted (temporally predictable condition) or not (temporally unpredictable condition) the onset of a target. In 25% of trials, an auditory signal occurred shortly after the target presentation, informing participants that they should inhibit their response completely. Response times were slower, and fewer inhibitory errors were made during temporally unpredictable than predictable trials, indicating enhanced control of unwanted actions when target onset time was unknown. Importantly, the ERN to inhibitory errors was greater in temporally unpredictable relative to temporally predictable conditions. Similarly, EMG data revealed prolonged MT when reactions to temporally unpredictable targets had not been stopped. Taken together, our results show that a temporally unpredictable environment increases the control of unwanted actions, both at cortical and peripheral levels, suggesting a higher subjective cost of maladaptive responses to temporally uncertain events.

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

BACKGROUND

Electromyography (EMG) is a classical technique used to record electrical activity associated with muscle contraction and is widely applied in Biomechanics, Biomedical Engineering, Neuroscience and Rehabilitation Robotics. Determining muscle activation onset timing, which can be used to infer movement intention and trigger prostheses and robotic exoskeletons, is still a big challenge. The main goal of this paper was to perform a review of the state-of-the-art of EMG onset detection methods. Moreover, we compared the performance of the most commonly used methods on experimental EMG data.

METHODS

A total of 156 papers published until March 2022 were included in the review. The papers were analyzed in terms of application domain, pre-processing method and EMG onset detection method. The three most commonly used methods [Single (ST), Double (DT) and Adaptive Threshold (AT)] were applied offline on experimental intramuscular and surface EMG signals obtained during contractions of ankle and knee joint muscles.

RESULTS

Threshold-based methods are still the most commonly used to detect EMG onset. Compared to ST and AT, DT required more processing time and, therefore, increased onset timing detection, when applied on experimental data. The accuracy of these three methods was high (maximum error detection rate of 7.3%), demonstrating their ability to automatically detect the onset of muscle activity. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset offline, reporting promising results.

CONCLUSIONS

This study organized and classified the existing EMG onset detection methods to create consensus towards a possible standardized method for EMG onset detection, which would also allow more reproducibility across studies. The three most commonly used methods (ST, DT and AT) proved to be accurate, while ST and AT were faster in terms of EMG onset detection time, especially when applied on intramuscular EMG data. These are important features towards movement intention identification, especially in real-time applications. Machine Learning methods have received increased attention as an alternative to detect muscle activation onset. However, although several methods have shown their capability offline, more research is required to address their full potential towards real-time applications, namely to infer movement intention.

Electrophysiological evidence of discontinuities in the propagation of lexical decision processes across the motor hierarchy

This research assessed the propagation of decisional effects across multiple electrophysiological indexes related to motor-response implementation within a lexical decision task, a paradigmatic case of a 2-alternative choice task on linguistic stimuli. By co-registering electroencephalographic and electromyographic data, we focused on the lexicality effect (i.e., the difference between responses to words and nonwords), and we tracked its influence across indexes of motor-response planning (indexed by effector-selective lateralization of beta-frequency desynchronizations), programming (indexed by the lateralized readiness potential) and execution (indexed by the chronometric durations of muscular responses). In addition, we explored corticomuscular coherence as the potential physiological underpinning of a continuous mapping of information between stimulus evaluation and response channels. The results revealed lexicality effects only on indexes of motor planning and execution, with no reliable involvement of the other measures. This pattern is discussed with reference to the hypothesis of multiple decisional components exerting different influences across the motor-hierarchy.

Developmental changes in impulse control: Trial-by-trial EMG dissociates the evolution of impulse strength from its subsequent suppression

Goal-oriented behavior can be disrupted by irrelevant information that automatically activates incorrect responses. While behavioral errors reveal response capture in such situations, they are only the tip of the iceberg. Additional subliminal activations of the incorrect responses (partial errors) can be revealed on correctly responded trials thanks to electromyography (EMG). In the current study, for the first time, EMG recorded in children was combined with distributional analyses. This allowed to investigate the properties of incorrect response activations and to highlight developmental changes in impulse control. A sample of 114 children aged 6-14 years was studied. Children performed a Simon task in which the irrelevant stimulus-position automatically activates a response that might be compatible or incompatible with the correct one. On incompatible trials, the automatic response activation must be overcome by controlled response selection. As previously observed in adults, our approach revealed the presence of an automatic EMG activation of the incorrect response elicited by the irrelevant stimulus dimension. Further, it revealed another independent source at the origin of incorrect response activations: the tendency to guess for response alternation. Both sources increased the frequency of early incorrect EMG activations, indicating impulsive responding. In addition, the influence of both sources decreased with increasing age. Thus, development is marked by improved ability to manage distractibility on the one hand and decreased tendency to rely on a guessing strategy on the other.

Speeding-up while growing-up: Synchronous functional development of motor and non-motor processes across childhood and adolescence

Describing the maturation of information processing in children is fundamental for developmental science. Although non-linear changes in reaction times have been well-documented, direct measurement of the development of the different processing components is lacking. In this study, electromyography was used to quantify the maturation of premotor and motor processes on a sample of 114 children (6–14 years-old) and 15 adults. Using a model-based approach, we show that the development of these two components is well-described by an exponential decrease in duration, with the decay rate being equal for the two components. These findings provide the first unbiased evidence in favour of the common developmental rate of nonmotor and motor processes by directly confronting rates of development of different processing components within the same task. This common developmental rate contrasts with the differential physical maturation of region-specific cerebral gray and white matter. Tentative paths of interpretation are proposed in the discussion.

Properties of the surface electromyogram following traumatic spinal cord injury: a scoping review

Traumatic spinal cord injury (SCI) disrupts spinal and supraspinal pathways, and this process is reflected in changes in surface electromyography (sEMG). sEMG is an informative complement to current clinical testing and can capture the residual motor command in great detail-including in muscles below the level of injury with seemingly absent motor activities. In this comprehensive review, we sought to describe how the sEMG properties are changed after SCI. We conducted a systematic literature search followed by a narrative review focusing on sEMG analysis techniques and signal properties post-SCI. We found that early reports were mostly focused on the qualitative analysis of sEMG patterns and evolved to semi-quantitative scores and a more detailed amplitude-based quantification. Nonetheless, recent studies are still constrained to an amplitude-based analysis of the sEMG, and there are opportunities to more broadly characterize the time- and frequency-domain properties of the signal as well as to take fuller advantage of high-density EMG techniques. We recommend the incorporation of a broader range of signal properties into the neurophysiological assessment post-SCI and the development of a greater understanding of the relation between these sEMG properties and underlying physiology. Enhanced sEMG analysis could contribute to a more complete description of the effects of SCI on upper and lower motor neuron function and their interactions, and also assist in understanding the mechanisms of change following neuromodulation or exercise therapy.

Preparing to React: A Behavioral Study on the Interplay between Proactive and Reactive Action Inhibition

Motor preparation, based on one's goals and expectations, allows for prompt reactions to stimulations from the environment. Proactive and reactive inhibitory mechanisms modulate this preparation and interact to allow a flexible control of responses. In this study, we investigate these two control mechanisms with an ad hoc cued Go/NoGo Simon paradigm in a within-subjects design, and by measuring subliminal motor activities through electromyographic recordings. Go cues instructed participants to prepare a response and wait for target onset to execute it (Go target) or inhibit it (NoGo target). Proactive inhibition keeps the prepared response in check, hence preventing false alarms. Preparing the cue-coherent effector in advance speeded up responses, even when it turned out to be the incorrect effector and reactive inhibition was needed to perform the action with the contralateral one. These results suggest that informative cues allow for the investigation of the interaction between proactive and reactive action inhibition. Partial errors' analysis suggests that their appearance in compatible conflict-free trials depends on cue type and prior preparatory motor activity. Motor preparation plays a key role in determining whether proactive inhibition is needed to flexibly control behavior, and it should be considered when investigating proactive/reactive inhibition.

Investigating the performance of an amplitude-independent algorithm for detecting the hand muscle activity of stroke survivors

To make robotic hand devices controlled by surface electromyography (sEMG) signals feasible and practical tools for assisting patients with hand impairments, the problems that prevent these devices from being widely used have to be overcome. The most significant problem is the involuntary amplitude variation of the sEMG signals due to the movement of electrodes during forearm motion. Moreover, for patients who have had a stroke or another neurological disease, the muscle activity of the impaired hand is weak and has a low signal-to-noise ratio (SNR). Thus, muscle activity detection methods intended for controlling robotic hand devices should not depend mainly on the amplitude characteristics of the sEMG signal in the detection process, and they need to be more reliable for sEMG signals that have a low SNR. Since amplitude-independent muscle activity detection methods meet these requirements, this paper investigates the performance of such a method on people who have had a stroke in terms of the detection of weak muscle activity and resistance to false alarms caused by the involuntary amplitude variation of sEMG signals; these two parameters are very important for achieving the reliable control of robotic hand devices intended for people with disabilities. A comparison between the performance of an amplitude-independent muscle activity detection algorithm and three amplitude-dependent algorithms was conducted by using sEMG signals recorded from six hemiparesis stroke survivors and from six healthy subjects. The results showed that the amplitude-independent algorithm performed better in terms of detecting weak muscle activity and resisting false alarms.

Mechanisms of Impulsive Responding to Temporally Predictable Events as Revealed by Electromyography

Temporal predictability optimises behaviour when a simple response is required, as demonstrated by faster reaction times (RTs) and higher accuracy. However, its beneficial effects come at a cost under situations of response conflict. Here, we investigated the motor underpinnings of behaviour to temporally predictable events in the Simon conflict task. We compared motor responses to lateralised targets whose position conflicted (incompatible condition) or not (compatible condition) with the hand of response. Importantly, electromyographic (EMG) recordings allowed us to study "partial errors", defined as subthreshold muscle activity in the incorrect response agonist preceding a correct response. Advanced distributional analyses coupled with EMG data revealed that temporal predictability induced impulsive premature responding, as indexed by increased likelihood of fast incorrect EMG activations (both partial errors and errors) to incompatible targets. In parallel, responding to temporally predictable targets speeded the latency of partial errors, further indicating that temporal predictability increased the tendency to act prematurely. There was, however, no effect of temporal predictability on subsequent suppression of partial errors. Our results provide direct evidence that temporal predictability acts by increasing the urge to initiate a fast, yet potentially erroneous, response. This mechanism parsimoniously explains both beneficial effects of temporal predictability when no conflict in the environment is present, as well as its costs when more complex motor behaviour is required.

Performance Evaluation of Fixed Sample Entropy in Myographic Signals for Inspiratory Muscle Activity Estimation

Fixed sample entropy (fSampEn) has been successfully applied to myographic signals for inspiratory muscle activity estimation, attenuating interference from cardiac activity. However, several values have been suggested for fSampEn parameters depending on the application, and there is no consensus standard for optimum values. This study aimed to perform a thorough evaluation of the performance of the most relevant fSampEn parameters in myographic respiratory signals, and to propose, for the first time, a set of optimal general fSampEn parameters for a proper estimation of inspiratory muscle activity. Different combinations of fSampEn parameters were used to calculate fSampEn in both non-invasive and the gold standard invasive myographic respiratory signals. All signals were recorded in a heterogeneous population of healthy subjects and chronic obstructive pulmonary disease patients during loaded breathing, thus allowing the performance of fSampEn to be evaluated for a variety of inspiratory muscle activation levels. The performance of fSampEn was assessed by means of the cross-covariance of fSampEn time-series and both mouth and transdiaphragmatic pressures generated by inspiratory muscles. A set of optimal general fSampEn parameters was proposed, allowing fSampEn of different subjects to be compared and contributing to improving the assessment of inspiratory muscle activity in health and disease.

Nondestructive Estimation of Muscle Contributions to STS Training with Different Loadings Based on Wearable Sensor System

Partial body weight support or loading sit-to-stand (STS) rehabilitation can be useful for persons with lower limb dysfunction to achieve movement again based on the internal residual muscle force and external assistance. To explicate how the muscles contribute to the kinetics and kinematics of STS performance by non-invasive in vitro detection and to nondestructively estimate the muscle contributions to STS training with different loadings, a wearable sensor system was developed with ground reaction force (GRF) platforms, motion capture inertial sensors and electromyography (EMG) sensors. To estimate the internal moments of hip, knee and ankle joints and quantify the contributions of individual muscle and gravity to STS movement, the inverse dynamics analysis on a simplified STS biomechanical model with external loading is proposed. The functional roles of the lower limb individual muscles (rectus femoris (RF), gluteus maximus (GM), vastus lateralis (VL), tibialis anterior (TA) and gastrocnemius (GAST)) during STS motion and the mechanism of the muscles' synergies to perform STS-specific subtasks were analyzed. The muscle contributions to the biomechanical STS subtasks of vertical propulsion, anteroposterior (AP) braking and propulsion for body balance in the sagittal plane were quantified by experimental studies with EMG, kinematic and kinetic data.

Onset and Offset Estimation of the Neural Inspiratory Time in Surface Diaphragm Electromyography: A Pilot Study in Healthy Subjects

This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (T_i/T_tot) decrements, from 0.54 to 0.18. The analysis of EMGdi signal amplitude is an alternative approach for the quantification of neural respiratory drive. The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdi_fse). The neural onset was detected through a dynamic threshold over the EMGdi_fse using the kernel density estimation method, while neural offset was detected by finding when the EMGdi_fse had decreased to 70% of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the T_i /T_tot protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the T_i/T _tot protocol. The relationship between pairs of RR values (Pearson's correlation coefficient of 0.99, Bland-=Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of T_i/T_tot values (Pearson's correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on noninvasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.