Changes of HD-sEMG maps of the upper limb during isometric endurance contractions.

Recent research in the field of surface EMG recorded with 2D electrode arrays have shown muscle adaptations as reflected on the spatial activation of motor units in response to pain, direction of movement or fatigue. The purpose of this study was to evaluate time- changes in the activation maps of upper limb muscles during endurance tasks associated with the degrees of freedom at the elbow joint. Preliminary results show time-variations in the distribution of intensity, that is, in the spatial recruitment of motor units, and that such changes may be dependent on the type of task.

Identification of isometric contractions based on High Density EMG maps.

Identification of motion intention and muscle activation strategy is necessary to control human-machine interfaces like prostheses or orthoses, as well as other rehabilitation devices, games and computer-based training programs. Pattern recognition from sEMG signals has been extensively investigated in the last decades, however, most of the studies did not take into account different strengths and EMG distributions associated to the intended task. The identification of such quantities could be beneficial for the training of the subject or the control of assistive devices. Recent studies have shown the need to improve pattern-recognition classification by reducing sensitivity to changes in the exerted strength, muscle-electrode shifts and bad contacts. Surface High Density EMG (HD-EMG) obtained from 2-dimensional arrays can provide much more information than electrode pairs for inferring not only motion intention but also the strategy adopted to distribute the load between muscles as well as changes in the spatial distribution of motor unit action potentials within a single muscle because of it. The objectives of this study were: (a) the automatic identification of four isometric motor tasks associated with the degrees of freedom of the forearm: flexion-extension and supination-pronation and (b) the differentiation among levels of voluntary contraction at low-medium efforts. For this purpose, monopolar HD-EMG maps were obtained from five muscles of the upper-limb in healthy subjects. An original classifier is proposed, based on: (1) Two steps linear discriminant analysis of the EMG information for each type of contraction, and (2) features extracted from HD-EMG maps and related to its intensity and distribution in the 2D space. The classifier was trained and tested with different effort levels. Spatial distribution-based features by themselves are not sufficient to classify the type of task or the effort level with an acceptable accuracy; however, when calculated with the "isolated masses" method proposed in this study and combined with intensity-base features, the performance of the classifier is improved. The classifier is capable of identifying the tasks even at 10% of Maximum Voluntary Contraction, in the range of effort level developed by patients with neuromuscular disorders, showing that intention end effort of motion can be estimated from HD-EMG maps and applied in rehabilitation.

A cross-sectional study comparing strength profile of dorsal and palmar flexor muscles of the wrist in epicondylitis and healthy men.

BACKGROUND: Strength training has been proposed by several authors to treat Lateral Epicondylitis. However, there is still a lack of information concerning muscle weakness and its relationship to imbalances and fatigability of forearm muscles during dynamic conditions in subjects after epicondylitis recovery. AIM: To analyze the relationship between lateral humeral epicondylitis, and forearm muscle strength and fatigue. SETTING: Rehabilitation specialized center POPULATION: Cross-sectional study in eight former epicondylitis men free of symptoms and actively working at the moment of the evaluation and eight healthy men volunteers. METHODS: Isokinetic tests were performed at different velocities in order to assess strength in concentric and eccentric contractions. Additionally, a long-term concentric test was carried out in order to analyze strength during endurance. The following variables were analyzed: Average torque of dorsal and palmar flexors of the wrist and ratio of agonist/antagonist for non-endurance contractions; length of initial and final plateaus and the slope of average torque decay during the endurance test. RESULTS: In both groups, average torque produced by palmar flexor muscles was higher than that produced by dorsal flexor muscles. Patients showed higher strength in palmar flexor muscles, whereas dorsal flexor strength was similar for both populations. Palmar flexor vs. dorsal flexor ratio was significantly higher in patients for eccentric contractions. Regarding fatigue, results showed that torque decreased earlier in patients. CONCLUSIONS AND CLINICAL REHABILITATION IMPACT: Both palmar flexor force and palmar/dorsal ratio in eccentric exercise were significantly higher in patients. This finding indicates a muscular imbalance in patients underlying the epicondylitis condition. Additionally, former patients fatigued earlier. Findings indicate that muscle imbalances and fatigability might be related to lateral epicondylitis. This information may be useful in the design and monitoring of programs intended for lateral epicondylitis rehabilitation. More studies are necessary to conclude if these differences are cause or consequence of the epicondylitis.

Cross-conditional entropy and coherence analysis of pharmaco-EEG changes induced by alprazolam.

RATIONALE: Quantitative analysis of electroencephalographic signals (EEG) and their interpretation constitute a helpful tool in the assessment of the bioavailability of psychoactive drugs in the brain. Furthermore, psychotropic drug groups have typical signatures which relate biochemical mechanisms with specific EEG changes. OBJECTIVES: To analyze the pharmacological effect of a dose of alprazolam on the connectivity of the brain during wakefulness by means of linear and nonlinear approaches. METHODS: EEG signals were recorded after alprazolam administration in a placebo-controlled crossover clinical trial. Nonlinear couplings assessed by means of corrected cross-conditional entropy were compared to linear couplings measured with the classical magnitude squared coherence. RESULTS: Linear variables evidenced a statistically significant drug-induced decrease, whereas nonlinear variables showed significant increases. All changes were highly correlated to drug plasma concentrations. The spatial distribution of the observed connectivity changes clearly differed from a previous study: changes before and after the maximum drug effect were mainly observed over the anterior half of the scalp. Additionally, a new variable with very low computational cost was defined to evaluate nonlinear coupling. This is particularly interesting when all pairs of EEG channels are assessed as in this study. CONCLUSIONS: Results showed that alprazolam induced changes in terms of uncoupling between regions of the scalp, with opposite trends depending on the variables: decrease in linear ones and increase in nonlinear features. Maps provided consistent information about the way brain changed in terms of connectivity being definitely necessary to evaluate separately linear and nonlinear interactions.

Robust outlier detection in high-density surface electromyographic signals.

High Density surface Electromyography (HDsEMG) has been applied in both research and clinical applications for non-invasive neuromuscular assessment in several different fields using 2-D array. Proper interpretation of HDsEMG signals requires identifying "good" channels (where there is no short-circuit or bad-contact or major power line interference problem). Recording with many channels usually implies bad-contacts (that introduces large power line interference) and short-circuits (when using gels). In addition to online monitoring the electrode-contact quality, it is necessary to identify "bad" channels, or outliers, prior to the analysis of HDsEMG signal. In this paper we introduce a robust method to identify outliers in a set of monopolar HDsEMG signals recorded from Biceps and Triceps Brachii, Anconeus, Brachioradialis and Pronator Teres. The sensitivity and precision of this method show that this approach is promising.