Rojas, M.; Alonso, J.F.; Jordanic, M.; Romero, S.; Mañanas, M.A. Revista iberoamericana de automática e informática industrial Vol. 14, num. 4, p. 406-411 DOI: 10.1016/j.riai.2017.07.006 Data de publicació: 2017-10-01 Article en revista
La identificación de tareas y estimación del movimiento voluntario basados en electromiografía (EMG) constituyen un problema conocido que involucra diferentes áreas en sistemas expertos, particularmente la de reconocimiento de patrones, con muchas aplicaciones posibles en dispositivos de asistencia y rehabilitación. La información que proporciona puede resultar útil para el control de exoesqueletos o brazos robóticos utilizados en terapias activas. La tecnología emergente de electromiografía de alta densidad (HD-EMG) abre nuevas posibilidades para extraer información neural y ya ha sido reportado que la distribución espacial de mapas de intensidad HD-EMG es una característica valiosa en la identificación de tareas isométricas (contracciones que no producen cambio en la longitud del músculo). Este estudio explora la utilización de la distribución espacial de la actividad mioeléctrica y lleva a cabo identificación de tareas durante ejercicios dinámicos a diferentes velocidades que son mucho más cercanos a los que se utilizan habitualmente en las terapias de rehabilitación. Con este objetivo, se registraron señales HD-EMG en un grupo de sujetos sanos durante la realización de un conjunto de tareas isométricas y dinámicas del miembro superior. Los resultados indican que la distribución espacial es una característica muy útil en la identificación, no solo de contracciones isométricas sino también de contracciones dinámicas, mejorando la eficiencia y naturalidad del control de dispositivos de rehabilitación para que se adapte mejor al usuario.
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.
Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.
The electromyography (EMG) signal is the summation of traveling motor unit (MU) action potentials that propagate along the fibers from the neuromuscular junction (Innervation Zone, IZ) to the tendons with a certain conduction velocity (CV) (Merletti and Parker, 2005). EMG signals can be detected using either intramuscular or surface electrodes. Intramuscular EMG (iEMG) signals involve the insertion of needles or fine wire electrodes into a muscle (Merletti et al., 2008). Surface EMG (sEMG) signals from the underlying muscles can be detected and unobtrusively on the skin all over the human body and they can be used in modeling movement intentions and in monitoring muscle function during rehabilitation processes (Zwarts and Stegeman, 2003).
Jordanic, M.; Rojas, M.; Mañanas, M.A.; Alonso, J.F. Journal of neural engineering Vol. 13, num. 4, p. 046002-1-046002-10 DOI: 10.1088/1741-2560/13/4/046002 Data de publicació: 2016-05-17 Article en revista
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
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.
Alonso, J.F.; Romero, S.; Mañanas, M.A.; Rojas, M.; Riba, J.; Barbanoj, M.J. Medical and biological engineering and computing Vol. 53, num. 10, p. 1011-1023 DOI: 10.1007/s11517-015-1315-6 Data de publicació: 2015-10-01 Article en revista
The identification of the brain regions involved in the neuropharmacological action is a potential procedure for drug development. These regions are commonly determined by the voxels showing significant statistical differences after comparing placebo-induced effects with drug-elicited effects. LORETA is an electroencephalography (EEG) source imaging technique frequently used to identify brain structures affected by the drug. The aim of the present study was to evaluate different methods for the correction of multiple comparisons in the LORETA maps. These methods which have been commonly used in neuroimaging and also simulated studies have been applied on a real case of pharmaco-EEG study where the effects of increasing benzodiazepine doses on the central nervous system measured by LORETA were investigated. Data consisted of EEG recordings obtained from nine volunteers who received single oral doses of alprazolam 0.25, 0.5, and 1 mg, and placebo in a randomized crossover double-blind design. The identification of active regions was highly dependent on the selected multiple test correction procedure. The combined criteria approach known as cluster mass was useful to reveal that increasing drug doses led to higher intensity and spread of the pharmacologically induced changes in intracerebral current density.
Carme, U.; Chaler, J.; Rojas, M.; Pujol, E.; Bertram, M.; Garreta, R.; Mañanas, M.A. European Journal of Physical and Rehabilitation Medicine Vol. 49, num. 4, p. 507-515 Data de publicació: 2013-08 Article en revista
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. […]
Rojas, M.; Alonso, J.F.; Chaler, J.; Mañanas, M.A. Annual International Conference of the IEEE Engineering in Medicine and Biology Society p. 5005-5008 DOI: 10.1109/EMBC.2013.6610672 Data de presentació: 2013-07 Presentació treball a congrés
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.
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.
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.
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.
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
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.
Rojas, M.; Mañanas, M.A.; Alonso, J.F.; Merletti, R. Journal of electromyography and kinesiology Vol. 23, num. 1, p. 33-42 DOI: 10.1016/j.jelekin.2012.06.009 Data de publicació: 2012-07 Article en revista
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 patternrecognition
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.
La señal EMG de superficie permite analizar cuantitativamente los cambios fisiológicos ocasionados por diferentes patologías ya sea sobre la Médula espinal, las Motoneuronas, la unión neuromuscular o los músculos propiamente dichos. Por tratarse de una técnica no invasiva, facilita el proceso de diagnóstico y monitorización de dichas enfermedades. Por otra parte, la EMG multicanal permite estudiar directamente
los determinantes fisiológicos de la fatiga muscular, relacionados con cambios a nivel celular que ocasionan variaciones en la conducción de los potenciales de acción sobre las fibras musculares. En este estudio se introducen los mecanismos de la contracción muscular, su relación con la señal EMG y se presentan dos ejemplos de aplicación en el estudio de patologías de la extremidad superior.
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
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.
Marateb, H.R.; Rojas, M.; Mansourian, M.; Merletti, R.; Mañanas, M.A. Medical and biological engineering and computing Vol. 50, num. 1, p. 79-89 DOI: 10.1007/s11517-011-0790-7 Data de publicació: 2012-01 Article en revista
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 nearzero
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.
Alonso, J.F.; Mañanas, M.A.; Rojas, M.; Bruce, E. Journal of electromyography and kinesiology Vol. 21, num. 6, p. 1064-1073 DOI: 10.1016/j.jelekin.2011.07.004 Data de publicació: 2011-12 Article en revista
Rojas, M.; Garcia, M.; Alonso, J.F.; Marin, J.; Mañanas, M.A. Revista iberoamericana de automática e informática industrial Vol. 8, num. 2, p. 35-44 DOI: 10.4995/RIAI.2011.02.06 Data de publicació: 2011-04-08 Article en revista
Marateb, H.R.; Rojas, M.; Mañanas, M.A.; Merletti, R. Annual International Conference of the IEEE Engineering in Medicine and Biology Society p. 4850-4853 DOI: 10.1109/IEMBS.2010.5627280 Data de presentació: 2010-09-01 Presentació treball a congrés
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.
La posición y el movimiento del cuerpo están controlados por señales eléctricas que viajan desde y hacia el Sistema Nervioso Central, produciendo la contracción de los músculos voluntarios.
Cuando se presenta una patología ya sea sobre la Médula espinal, las Motoneuronas, la unión neuromuscular o los músculos propiamente dichos, se generan ciertas variaciones sobre la propagación eléctrica y la morfología de dichas señales. Estas variaciones pueden ser observadas y cuantificadas por medio de señales de electromiografía. Más aún, si se utilizan técnicas no invasivas de detección en la superficie de la piel, se facilita el proceso diagnóstico y monitorización de este tipo de enfermedades. La EMG multicanal permite estudiar los determinantes fisiológicos de la fatiga muscular y el análisis de la actividad de unidades motoras aisladas. Dicha información resulta de gran ayuda para la valoración y mejora de los procesos de rehabilitación motora.