Repetitive active and passive cognitive stimulations induce EEG changes in patients with Rett syndrome.

BACKGROUND: Despite being considered a rare disease, Rett syndrome is a leading cause of profound cognitive impairment in females. This study explores game-based cognitive stimulation to enhance attention during learning tasks, offering an alternative treatment perspective. METHODS: Fifteen diagnosed Rett syndrome girls participated in four 24-minute sessions, including a 5-minute initial resting state recording. Primary indicators for analysis included relative power and spectral entropy. RESULTS: Significant findings indicated variations among conditions (resting state, active task, passive task) in response to stimulation. Notably, over four days, evolution occurred, characterized by decreasing delta power and increasing theta and beta power. Topographic maps confirmed these shifts, highlighting affected brain areas. Linear regression emphasized the most significant impact on the first day, with subsequent shifts towards higher frequencies, particularly during the resting state. By the fourth day, resting-state patterns resembled those during cognitive activities. CONCLUSION: Findings suggest cognitive stimulation induces substantial EEG spectral changes, potentially linked to cognitive enhancements in Rett syndrome. The shift towards higher frequency bands and increased spectral entropy align with enhanced brain activation during cognitive sessions, underscoring the potential of cognitive stimulation therapies and calling for further research to optimize abilities in individuals with Rett syndrome. IMPACT: Game-based cognitive stimulation induces substantial EEG changes in individuals with Rett syndrome, enhancing cognitive functions, notably attention during learning. This study conducts a distinctive examination, assessing the habituation paradigm through the combination of game-based cognitive stimulation and learning, providing valuable insights into enhancing attention in Rett syndrome. Impacting understanding of cognitive processes in Rett syndrome, this research reveals significant EEG variations during tasks, emphasizing the potential of cognitive stimulation for attention enhancement and the need for further research in tailored interventions.

High-density surface electromyography maps after computer-aided training in individual with congenital transverse deficiency: a case study.

BACKGROUND: The aim of this study was to determine whether computer-aided training (CAT) of motor tasks would increase muscle activity and change its spatial distribution in a patient with a bilateral upper-limb congenital transverse deficiency. We believe that our study makes a significant contribution to the literature because it demonstrates the usefulness of CAT in promoting the neuromuscular adaptation in people with congenital limb deficiencies and altered body image. CASE PRESENTATION: The patient with bilateral upper-limb congenital transverse deficiency and the healthy control subject performed 12 weeks of the CAT. The subject's task was to imagine reaching and grasping a book with the hand. Subjects were provided a visual animation of that movement and sensory feedback to facilitate the mental engagement to accomplish the task. High-density electromyography (HD-EMG; 64-electrode) were collected from the trapezius muscle during a shrug isometric contraction before and after 4, 8, 12 weeks of the training. After training, we observed in our patient changes in the spatial distribution of the activation, and the increased average intensity of the EMG maps and maximal force. CONCLUSIONS: These results, although from only one patient, suggest that mental training supported by computer-generated visual and sensory stimuli leads to beneficial changes in muscle strength and activity. The increased muscle activation and changed spatial distribution of the EMG activity after mental training may indicate the training-induced functional plasticity of the motor activation strategy within the trapezius muscle in individual with bilateral upper-limb congenital transverse deficiency. Marked changes in spatial distribution during the submaximal contraction in the patient after training could be associated with changes of the neural drive to the muscle, which corresponds with specific (unfamiliar for patient) motor task. These findings are relevant to neuromuscular functional rehabilitation in patients with a bilateral upper-limb congenital transverse deficiency especially before and after upper limb transplantation and to development of the EMG based prostheses.

A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography.

Estimation of neuromuscular intention using electromyography (EMG) and pattern recognition is still an open problem. One of the reasons is that the pattern-recognition approach is greatly influenced by temporal changes in electromyograms caused by the variations in the conductivity of the skin and/or electrodes, or physiological changes such as muscle fatigue. This paper proposes novel features for task identification extracted from the high-density electromyographic signal (HD-EMG) by applying the mean shift channel selection algorithm evaluated using a simple and fast classifier-linear discriminant analysis. HD-EMG was recorded from eight subjects during four upper-limb isometric motor tasks (flexion/extension, supination/pronation of the forearm) at three different levels of effort. Task and effort level identification showed very high classification rates in all cases. This new feature performed remarkably well particularly in the identification at very low effort levels. This could be a step towards the natural control in everyday applications where a subject could use low levels of effort to achieve motor tasks. Furthermore, it ensures reliable identification even in the presence of myoelectric fatigue and showed robustness to temporal changes in EMG, which could make it suitable in long-term applications.

Spatial distribution of HD-EMG improves identification of task and force in patients with incomplete spinal cord injury.

BACKGROUND: Recent studies show that spatial distribution of High Density surface EMG maps (HD-EMG) improves the identification of tasks and their corresponding contraction levels. However, in patients with incomplete spinal cord injury (iSCI), some nerves that control muscles are damaged, leaving some muscle parts without an innervation. Therefore, HD-EMG maps in patients with iSCI are affected by the injury and they can be different for every patient. The objective of this study is to investigate the spatial distribution of intensity in HD-EMG recordings to distinguish co-activation patterns for different tasks and effort levels in patients with iSCI. These patterns are evaluated to be used for extraction of motion intention. METHOD: HD-EMG was recorded in patients during four isometric tasks of the forearm at three different effort levels. A linear discriminant classifier based on intensity and spatial features of HD-EMG maps of five upper-limb muscles was used to identify the attempted tasks. Task and force identification were evaluated for each patient individually, and the reliability of the identification was tested with respect to muscle fatigue and time interval between training and identification. RESULTS: Three feature sets were analyzed in the identification: 1) intensity of the HD-EMG map, 2) intensity and center of gravity of HD-EMG maps and 3) intensity of a single differential EMG channel (gold standard). Results show that the combination of intensity and spatial features in classification identifies tasks and effort levels properly (Acc = 98.8 %; S = 92.5 %; P = 93.2 %; SP = 99.4 %) and outperforms significantly the other two feature sets (p < 0.05). CONCLUSION: In spite of the limited motor functionality, a specific co-activation pattern for each patient exists for both intensity, and spatial distribution of myoelectric activity. The spatial distribution is less sensitive than intensity to myoelectric changes that occur due to fatigue, and other time-dependent influences.

Spinal Cord Injury Patients Exhibit Changes in Motor-Related Activity and Topographic Distribution.

Spinal Cord Injury (SCI) is a common disease that usually limits the patient's independence by affecting their motor function. SCI patients usually present neuroplasticity, which allows brain signals transmission through spread pathways. Some innovative rehabilitation therapies, such as functional electrical stimulation (FES) or Brain-computer interfaces (BCIs) jointly with motor neuroprostheses, provide hope for functional restoration. BCIs require the analysis of event-related EEG potentials (ERPs). Movement-related cortical potentials (MRCPs) and event-related desynchroni-zation and synchronization (ERD/ERS) are the most commonly studied ERPs during motor activity. ERPs of healthy subjects may vary from SCI patients. Thus, this study aimed to compare ERPs between healthy subjects and SCI patients during upper-limb movements (forearm supination and pronation, and hand open). Differences between controls and SCI patients were shown in terms of ERPs' amplitude as well as in topographic maps. Changes in amplitude were more substantial in ERD potentials than in MRCPs, while topographic maps showed better localization of all features in healthy patients. The level of SCI injury determines the patients' mobility. A comparison between complete, partial and no motor function subjects showed lower values of feature's amplitudes in the latter group.Clinical Relevance- This demonstrates the existence of significant statistical differences between healthy and SCI subjects, and might be helpful when performing SCI rehabilitation techniques such as designing BCI and neuroprostheses, or analyzing and understanding the brain plasticity process.

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs.

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

A new force profile signal for a convex solution of muscle force estimation from electromyographic signals.

High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique for measuring the electrical activity of a muscle with multiple, closely spaced electrodes. Estimation of muscle force is one of the applications of HD-sEMG. Usually, validating different EMG-Force models entails simple movements limited to laboratory settings. The validity of these models in more ecological conditions, requesting force production over a wide frequency band, remains unknown. In this study, we, therefore, compare the results of force prediction using four different types of input force profiles that can be representative of daily life activities, and we investigate whether the crest factor of these different input signals affects force prediction. For predicting the force from sEMG signals, we used our real-time and convex methods. HD-sEMG signals were recorded with 144 channels from the biceps brachii, brachioradialis, and triceps (long, lateral, and medial head) muscles of 24 healthy subjects during random signal, random phase, Schroeder phase, and minimum crest factor (crestmin) signal. The correlation and coefficient of determination (R2) between measured and predicted forces were calculated for the different force feedback profiles. The crestmin signal showed significantly better results based on statistical tests (P-value < 0.05), with correlation and R2 equal to 0.92±0.03 and 0.86±0.05, respectively. The results demonstrate that the crest factor of input signals is a crucial parameter that can impact the performance of EMG-Force models and must be considered during training.Clinical Relevance- This study demonstrates that lower crest factor multisine force profiles result in improved fitness for force prediction and can be used as an alternative to random signals.

High-density surface EMG maps from upper-arm and forearm muscles.

BACKGROUND: sEMG signal has been widely used in different applications in kinesiology and rehabilitation as well as in the control of human-machine interfaces. In general, the signals are recorded with bipolar electrodes located in different muscles. However, such configuration may disregard some aspects of the spatial distribution of the potentials like location of innervation zones and the manifestation of inhomogineties in the control of the muscular fibers. On the other hand, the spatial distribution of motor unit action potentials has recently been assessed with activation maps obtained from High Density EMG signals (HD-EMG), these lasts recorded with arrays of closely spaced electrodes. The main objective of this work is to analyze patterns in the activation maps, associating them with four movement directions at the elbow joint and with different strengths of those tasks. Although the activation pattern can be assessed with bipolar electrodes, HD-EMG maps could enable the extraction of features that depend on the spatial distribution of the potentials and on the load-sharing between muscles, in order to have a better differentiation between tasks and effort levels. METHODS: An experimental protocol consisting of isometric contractions at three levels of effort during flexion, extension, supination and pronation at the elbow joint was designed and HD-EMG signals were recorded with 2D electrode arrays on different upper-limb muscles. Techniques for the identification and interpolation of artifacts are explained, as well as a method for the segmentation of the activation areas. In addition, variables related to the intensity and spatial distribution of the maps were obtained, as well as variables associated to signal power of traditional single bipolar recordings. Finally, statistical tests were applied in order to assess differences between information extracted from single bipolar signals or from HD-EMG maps and to analyze differences due to type of task and effort level. RESULTS: Significant differences were observed between EMG signal power obtained from single bipolar configuration and HD-EMG and better results regarding the identification of tasks and effort levels were obtained with the latter. Additionally, average maps for a population of 12 subjects were obtained and differences in the co-activation pattern of muscles were found not only from variables related to the intensity of the maps but also to their spatial distribution. CONCLUSIONS: Intensity and spatial distribution of HD-EMG maps could be useful in applications where the identification of movement intention and its strength is needed, for example in robotic-aided therapies or for devices like powered- prostheses or orthoses. Finally, additional data transformations or other features are necessary in order to improve the performance of tasks identification.

High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans.

This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four different isometric tasks at different effort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from five upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classification methods. About 6% of the total number of signals were identified as "bad" channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identification of effort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identification among other uses.

Analysis of Muscle Load-Sharing in Patients With Lateral Epicondylitis During Endurance Isokinetic Contractions Using Non-linear Prediction.

The aim of this paper is to analyze muscle load-sharing in patients with Lateral Epicondylitis during dynamic endurance contractions by means of non-linear prediction of surface EMG signals. The proposed non-linear cross-prediction scheme was used to predict the envelope of an EMG signal and is based on locally linear models built in a lag-embedded Euclidean space. The results were compared with a co-activation index, a common measure based on the activation of a muscle pair. Non-linear prediction revealed changes in muscle coupling, that is load-sharing, over time both in a control group and Lateral Epicondylitis (p < 0.05), even when subjects did not report pain at the end of the exercise. These changes were more pronounced in patients, especially in the first part of the exercise and up to 50% of the total endurance time (p < 0.05). By contrast, the co-activation index showed no differences between groups. Results reflect the changing nature of muscular activation strategy, presumably because of the mechanisms triggered by fatigue. Strategies differ between controls and patients, pointing to an altered coordination in Lateral Epicondylitis.

Prediction of isometric motor tasks and effort levels based on high-density EMG in patients with incomplete spinal cord injury.

OBJECTIVE: The development of modern assistive and rehabilitation devices requires reliable and easy-to-use methods to extract neural information for control of devices. Group-specific pattern recognition identifiers are influenced by inter-subject variability. Based on high-density EMG (HD-EMG) maps, our research group has already shown that inter-subject muscle activation patterns exist in a population of healthy subjects. The aim of this paper is to analyze muscle activation patterns associated with four tasks (flexion/extension of the elbow, and supination/pronation of the forearm) at three different effort levels in a group of patients with incomplete Spinal Cord Injury (iSCI). APPROACH: Muscle activation patterns were evaluated by the automatic identification of these four isometric tasks along with the identification of levels of voluntary contractions. Two types of classifiers were considered in the identification: linear discriminant analysis and support vector machine. MAIN RESULTS: Results show that performance of classification increases when combining features extracted from intensity and spatial information of HD-EMG maps (accuracy = 97.5%). Moreover, when compared to a population with injuries at different levels, a lower variability between activation maps was obtained within a group of patients with similar injury suggesting stronger task-specific and effort-level-specific co-activation patterns, which enable better prediction results. SIGNIFICANCE: Despite the challenge of identifying both the four tasks and the three effort levels in patients with iSCI, promising results were obtained which support the use of HD-EMG features for providing useful information regarding motion and force intention.

Analysis of muscle coupling during isokinetic endurance contractions by means of nonlinear prediction.

Isokinetic exercises have been extensively used in order to analyze muscle imbalances and changes associated with fatigue. It is known that such changes are difficult to assess from EMG signals during dynamic contractions, especially, using linear signal processing tools. The aim of this work was to use nonlinear prediction in order to analyze muscle couplings and interactions in this context and to assess the load-sharing of different muscles during fatigue. Results show promising for detecting interaction strategies between muscles and even for the interaction between muscles and the output torque during endurance tests.

Outlier detection in high-density surface electromyographic signals.

Recently developed techniques allow the analysis of surface EMG in multiple locations over the skin surface (high-density surface electromyography, HDsEMG). The detected signal includes information from a greater proportion of the muscle of interest than conventional clinical EMG. However, recording with many electrodes simultaneously often implies bad-contacts, which introduce large power-line interference in the corresponding channels, and short-circuits that cause near-zero single differential signals when using gel. Such signals are called 'outliers' in data mining. In this work, outlier detection (focusing on bad contacts) is discussed for monopolar HDsEMG signals and a new method is proposed to identify 'bad' channels. The overall performance of this method was tested using the agreement rate against three experts' opinions. Three other outlier detection methods were used for comparison. The training and test sets for such methods were selected from HDsEMG signals recorded in Triceps and Biceps Brachii in the upper arm and Brachioradialis, Anconeus, and Pronator Teres in the forearm. The sensitivity and specificity of this algorithm were, respectively, 96.9 ± 6.2 and 96.4 ± 2.5 in percent in the test set (signals registered with twenty 2D electrode arrays corresponding to a total of 2322 channels), showing that this method is promising.