Farina, Dario

[["dc.bibliographiccitation.firstpage","1699"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Neurophysiology"],["dc.bibliographiccitation.lastpage","1706"],["dc.bibliographiccitation.volume","119"],["dc.contributor.author","Yavuz, Utku Ş."],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Diedrichs, Robin"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-02-06T11:53:32Z"],["dc.date.available","2019-02-06T11:53:32Z"],["dc.date.issued","2018"],["dc.description.abstract","Motor neurons innervating antagonist muscles receive reciprocal inhibitory afferent inputs to facilitate the joint movement in the two directions. The present study investigates the mutual transmission of reciprocal inhibitory afferent inputs between the tibialis anterior (TA) and triceps surae (soleus and medial gastrocnemius) motor units. We assessed this mutual mechanism in large populations of motor units for building a statistical distribution of the inhibition amplitudes during standardized input to the motor neuron pools to minimize the effect of modulatory pathways. Single motor unit activities were identified using high-density surface electromyography (HDsEMG) recorded from the TA, soleus (Sol), and medial gastrocnemius (GM) muscles during isometric dorsi- and plantarflexion. Reciprocal inhibition on the antagonist muscle was elicited by electrical stimulation of the tibial (TN) or common peroneal nerves (CPN). The probability density distributions of reflex strength for each muscle were estimated to examine the strength of mutual transmission of reciprocal inhibitory input. The results showed that the strength of reciprocal inhibition in the TA motor units was fourfold greater than for the GM and the Sol motor units. This suggests an asymmetric transmission of reciprocal inhibition between ankle extensor and flexor muscles. This asymmetry cannot be explained by differences in motor unit type composition between the investigated muscles since we sampled low-threshold motor units in all cases. Therefore, the differences observed for the strength of inhibition are presumably due to a differential reciprocal spindle afferent input and the relative contribution of nonreciprocal inhibitory pathways. NEW & NOTEWORTHY We investigated the mutual transmission of reciprocal inhibition in large samples of motor units using a standardized input (electrical stimulation) to the motor neurons. The results demonstrated that the disynaptic reciprocal inhibition exerted between ankle flexor and extensor muscles is asymmetric. The functional implication of asymmetric transmission may be associated with the neural strategies of postural control."],["dc.identifier.doi","10.1152/jn.00424.2017"],["dc.identifier.pmid","29384455"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/57534"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.relation.eissn","1522-1598"],["dc.title","Reciprocal inhibition between motor neurons of the tibialis anterior and triceps surae in humans"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1368"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Wiley Interdisciplinary Reviews Systems Biology and Medicine"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Fernandez, J. W."],["dc.contributor.author","Modenese, L."],["dc.contributor.author","Carty, Christopher P."],["dc.contributor.author","Barber, L. A."],["dc.contributor.author","Oberhofer, K."],["dc.contributor.author","Zhang, J."],["dc.contributor.author","Handsfield, G. G."],["dc.contributor.author","Stott, N. S."],["dc.contributor.author","Besier, Thor F."],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Lloyd, David G."],["dc.date.accessioned","2018-11-07T10:27:01Z"],["dc.date.available","2018-11-07T10:27:01Z"],["dc.date.issued","2017"],["dc.description.abstract","This position paper proposes a modeling pipeline to develop clinically relevant neuromusculoskeletal models to understand and treat complex neurological disorders. Although applicable to a variety of neurological conditions, we provide direct pipeline applicative examples in the context of cerebral palsy (CP). This paper highlights technologies in: (1) patient-specific segmental rigid body models developed from magnetic resonance imaging for use in inverse kinematics and inverse dynamics pipelines; (2) efficient population-based approaches to derive skeletal models and muscle origins/insertions that are useful for population statistics and consistent creation of continuum models; (3) continuum muscle descriptions to account for complex muscle architecture including spatially varying material properties with muscle wrapping; (4) muscle and tendon properties specific to CP; and (5) neural-based electromyography-informed methods for muscle force prediction. This represents a novel modeling pipeline that couples for the first time electromyography extracted features of disrupted neuromuscular behavior with advanced numerical methods for modeling CP-specific musculoskeletal morphology and function. The translation of such pipeline to the clinical level will provide a new class of biomarkers that objectively describe the neuromusculoskeletal determinants of pathological locomotion and complement current clinical assessment techniques, which often rely on subjective judgment. (C) 2016 Wiley Periodicals, Inc."],["dc.identifier.doi","10.1002/wsbm.1368"],["dc.identifier.isi","000394898500001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43161"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Wiley"],["dc.relation.issn","1939-005X"],["dc.relation.issn","1939-5094"],["dc.title","Toward modeling locomotion using electromyography-informed 3D models: application to cerebral palsy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","37"],["dc.bibliographiccitation.journal","Acta Physiologica"],["dc.bibliographiccitation.lastpage","38"],["dc.bibliographiccitation.volume","215"],["dc.contributor.author","Yavuz, Utku Suekrue"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Sebik, Oguz"],["dc.contributor.author","Holobar, Ales"],["dc.contributor.author","Froemmel, Cornelius"],["dc.contributor.author","Turker, Kemal S."],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:49:23Z"],["dc.date.available","2018-11-07T09:49:23Z"],["dc.date.issued","2015"],["dc.identifier.isi","000364786400081"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35500"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.publisher.place","Hoboken"],["dc.relation.issn","1748-1716"],["dc.relation.issn","1748-1708"],["dc.title","The new technique for accurate estimation of the spinal cord circuitry: recording reflex responses of large motor unit populations"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","189"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","198"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Stango, Antonietta"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T10:00:16Z"],["dc.date.available","2018-11-07T10:00:16Z"],["dc.date.issued","2015"],["dc.description.abstract","Research on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods similar to 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift (+/- 1 cm) with respect to the classic features (p<0.05). When even just one channel was noisy, the classification accuracy dropped by similar to 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space proposed in this study improved the robustness to electrode number and shift in myocontrol with respect to previous approaches."],["dc.description.sponsorship","ERC AdvancedGrant DE-MOVE [267888]"],["dc.identifier.doi","10.1109/TNSRE.2014.2366752"],["dc.identifier.isi","000351365100005"],["dc.identifier.pmid","25389242"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37767"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1558-0210"],["dc.relation.issn","1534-4320"],["dc.title","Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1446"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","IEEE Transactions on Signal Processing"],["dc.bibliographiccitation.lastpage","1459"],["dc.bibliographiccitation.volume","60"],["dc.contributor.author","Gianfelici, Francesco"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:13:12Z"],["dc.date.available","2018-11-07T09:13:12Z"],["dc.date.issued","2012"],["dc.description.abstract","This paper proposes a general framework that is able to define a set of classification algorithms for brain-computer interfacing (BCI). We define a distributed representation of the EEG based on multichannel autoregressive models. In a subsequent step, we extend this multichannel modeling in a combinatoric setting, which is able to describe with a class of nonlinear combinatoric operators the embedded relationships that the EEG shows in the manifolds. The generality and the flexibility of the nonlinear combinatoric operators and their mathematical properties allow the design of an indefinite number of classification algorithms each displaying relevant properties, such as linearity with respect to the parameters, noise rejection, low computational complexity of the classification procedure. In such a way, we obtain an intuitive and rigorous way to design new BCI algorithms. As an example of this theoretical framework, we present a novel classification algorithm based on four properties of this nonlinear combinatoric operator. The method was validated on the classification of single-trial EEG signals recorded during motor imagination, and it was compared on two additional standard datasets obtained from the BCI competition, with other feature extraction and classification techniques based on common spatial pattern, common spatial subspace decomposition and Fisher discriminant analysis, linear discriminant analysis, Markov chains, and expectation maximization. In conclusion, the proposed framework is suited for a broad number of BCI applications."],["dc.identifier.doi","10.1109/TSP.2011.2174791"],["dc.identifier.isi","000300424500037"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27121"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1053-587X"],["dc.title","An Effective Classification Framework for Brain-Computer Interfacing Based on a Combinatoric Setting"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2092"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","IEEE Transactions on Biomedical Engineering"],["dc.bibliographiccitation.lastpage","2101"],["dc.bibliographiccitation.volume","61"],["dc.contributor.author","Xu, Ren"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Mrachacz-Kersting, Natalie"],["dc.contributor.author","Lin, Chuang"],["dc.contributor.author","Asin Prieto, Guillermo"],["dc.contributor.author","Moreno, Juan C."],["dc.contributor.author","Pons, Jose L."],["dc.contributor.author","Dremstrup, Kim"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:38:30Z"],["dc.date.available","2018-11-07T09:38:30Z"],["dc.date.issued","2014"],["dc.description.abstract","In this paper, we present a brain-computer interface (BCI) driven motorized ankle-foot orthosis (BCI-MAFO), intended for stroke rehabilitation, and we demonstrate its efficacy in inducing cortical neuroplasticity in healthy subjects with a short intervention procedure (similar to 15 min). This system detects imaginary dorsiflexion movements within a short latency from scalp EEG through the analysis of movement-related cortical potentials (MRCPs). A manifold-based method, called locality preserving projection, detected the motor imagery online with a true positive rate of 73.0 +/- 10.3%. Each detection triggered the MAFO to elicit a passive dorsiflexion. In nine healthy subjects, the size of the motor-evoked potential (MEP) elicited by transcranial magnetic stimulation increased significantly immediately following and 30 min after the cessation of this BCI-MAFO intervention for similar to 15 min (p = 0.009 and p < 0.001, respectively), indicating neural plasticity. In four subjects, the size of the short latency stretch reflex component did not change following the intervention, suggesting that the site of the induced plasticity was cortical. All but one subject also performed two control conditions where they either imagined only or where the MAFO was randomly triggered. Both of these control conditions resulted in no significant changes in MEP size (p = 0.38 and p = 0.15). The proposed system provides a fast and effective approach for inducing cortical plasticity through BCI and has potential in motor function rehabilitation following stroke."],["dc.description.sponsorship","EU project BETTER [247935]; China Scholarship Council [201204910155]"],["dc.identifier.doi","10.1109/TBME.2014.2313867"],["dc.identifier.isi","000337808200018"],["dc.identifier.pmid","24686231"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33075"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1558-2531"],["dc.relation.issn","0018-9294"],["dc.title","A Closed-Loop Brain-Computer Interface Triggering an Active Ankle-Foot Orthosis for Inducing Cortical Neural Plasticity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","81"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","90"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Yao, Lin"],["dc.contributor.author","Sheng, Xinjun"],["dc.contributor.author","Zhang, Dingguo"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Zhu, Xiangyang"],["dc.date.accessioned","2020-12-10T18:26:21Z"],["dc.date.available","2020-12-10T18:26:21Z"],["dc.date.issued","2017"],["dc.description.abstract","We propose and test a novel brain-computer interface (BCI) based on imagined tactile sensation. During an imagined tactile sensation, referred to as somatosensory attentional orientation (SAO), the subject shifts and maintains somatosensory attention on a body part, e.g., left or right hand. The SAO can be detected from EEG recordings for establishing a communication channel. To test for the hypothesis that SAO on different body parts can be discriminated from EEG, 14 subjects were assigned to a group who received an actual sensory stimulation (STE-Group), and 18 subjects were assigned to the SAO only group (SAO-Group). In single trials, the STE-Group received tactile stimulation first (both wrists simultaneously stimulated), and then maintained the attention on the selected body part (without stimulation). The same group also performed the SAO task first and then received the tactile stimulation. Conversely, the SAO-Group performed SAO without any stimulation, neither before nor after the SAO. In both the STE-Group and SAO-Group, it was possible to identify the SAO-related oscillatory activation that corresponded to a contralateral event-related desynchronization (ERD) stronger than the ipsilateral ERD. Discriminative information, represented as R-2, was found mainly on the somatosensory area of the cortex. In the STE-Group, the average classification accuracy of SAO was 83.6%, and it was comparable with tactile BCI based on selective sensation (paired-t test, P > 0.05). In the SAO-Group the average online performancewas 75.7%. For this group, after frequency band selection the offline performance reached 82.5% on average, with >= 80% for 12 subjects and >= 95% for four subjects. Complementary to tactile sensation, the SAO does not require sensory stimulation, with the advantage of being completely independent from the stimulus."],["dc.identifier.doi","10.1109/TNSRE.2016.2572226"],["dc.identifier.eissn","1558-0210"],["dc.identifier.isi","000396396900009"],["dc.identifier.issn","1534-4320"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76050"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Ieee-inst Electrical Electronics Engineers Inc"],["dc.relation.issn","1558-0210"],["dc.relation.issn","1534-4320"],["dc.title","A BCI System Based on Somatosensory Attentional Orientation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","628"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","Clinical Journal of Pain"],["dc.bibliographiccitation.lastpage","634"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Falla, Deborah"],["dc.contributor.author","O'Leary, Shaun"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Jull, Gwendolen"],["dc.date.accessioned","2018-11-07T09:06:51Z"],["dc.date.available","2018-11-07T09:06:51Z"],["dc.date.issued","2012"],["dc.description.abstract","Objectives: Altered activation of the deep cervical flexors (longus colli and longus capitis) has been found in individuals with neck pain disorders but the response to training has been variable. Therefore, this study investigated the relationship between change in deep cervical flexor muscle activity and symptoms in response to specific training. Methods: Fourteen women with chronic neck pain undertook a 6-week program of specific training that consisted of a craniocervical flexion exercise performed twice per day (10 to 20 min) for the duration of the trial. The exercise targets the deep flexor muscles of the upper cervical region. At baseline and follow-up, measures were taken of neck pain intensity (visual analogue scale, 0 to 10), perceived disability (Neck Disability Index, 0 to 50) and electromyography (EMG) of the deep cervical flexors (by a nasopharyngeal electrode suctioned over the posterior oropharyngeal wall) during performance of craniocervical flexion. Results: After training, the activation of the deep cervical flexors increased (P < 0.0001) with the greatest change occurring in patients with the lowest values of deep cervical flexor EMG amplitude at baseline (R-2 = 0.68; P < 0.001). There was a significant relationship between initial pain intensity, change in pain level with training, and change in EMG amplitude for the deep cervical flexors during craniocervical flexion (R-2 = 0.34; P < 0.05). Discussion: Specific training of the deep cervical flexor muscles in women with chronic neck pain reduces pain and improves the activation of these muscles, especially in those with the least activation of their deep cervical flexors before training. This finding suggests that the selection of exercise based on a precise assessment of the patients' neuromuscular control and targeted exercise interventions based on this assessment are likely to be the most beneficial to patients with neck pain."],["dc.description.sponsorship","NHMRC of Australia"],["dc.identifier.doi","10.1097/AJP.0b013e31823e9378"],["dc.identifier.isi","000307640500011"],["dc.identifier.pmid","22156825"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25644"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Lippincott Williams & Wilkins"],["dc.relation.issn","0749-8047"],["dc.title","The Change in Deep Cervical Flexor Activity After Training Is Associated With the Degree of Pain Reduction in Patients With Chronic Neck Pain"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2509"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of Neurophysiology"],["dc.bibliographiccitation.lastpage","2527"],["dc.bibliographiccitation.volume","114"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Maculan, Marco"],["dc.contributor.author","Pizzolato, Claudio"],["dc.contributor.author","Reggiani, Monica"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:50:44Z"],["dc.date.available","2018-11-07T09:50:44Z"],["dc.date.issued","2015"],["dc.description.abstract","This work presents an electrophysiologically and dynamically consistent musculoskeletal model to predict stiffness in the human ankle and knee joints as derived from the joints constituent biological tissues (i.e., the spanning musculotendon units). The modeling method we propose uses electromyography (EMG) recordings from 13 muscle groups to drive forward dynamic simulations of the human leg in five healthy subjects during over-ground walking and running. The EMG-driven musculoskeletal model estimates musculotendon and resulting joint stiffness that is consistent with experimental EMG data as well as with the experimental joint moments. This provides a framework that allows for the first time observing 1) the elastic interplay between the knee and ankle joints, 2) the individual muscle contribution to joint stiffness, and 3) the underlying co-contraction strategies. It provides a theoretical description of how stiffness modulates as a function of muscle activation, fiber contraction, and interacting tendon dynamics. Furthermore, it describes how this differs from currently available stiffness definitions, including quasi-stiffness and short-range stiffness. This work offers a theoretical and computational basis for describing and investigating the neuromuscular mechanisms underlying human locomotion."],["dc.identifier.doi","10.1152/jn.00989.2014"],["dc.identifier.isi","000363548300041"],["dc.identifier.pmid","26245321"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35769"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physiological Soc"],["dc.relation.issn","1522-1598"],["dc.relation.issn","0022-3077"],["dc.title","Modeling and simulating the neuromuscular mechanisms regulating ankle and knee joint stiffness during human locomotion"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","066015"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Journal of Neural Engineering"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Marateb, Hamid Reza"],["dc.contributor.author","Muceli, Silvia"],["dc.contributor.author","McGill, Kevin C."],["dc.contributor.author","Merletti, Roberto"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T08:49:24Z"],["dc.date.available","2018-11-07T08:49:24Z"],["dc.date.issued","2011"],["dc.description.abstract","This paper presents a density-based method to automatically decompose single-channel intramuscular electromyogram (EMG) signals into their component motor unit action potential (MUAP) trains. In contrast to most previous decomposition methods, which require pre-setting and (or) tuning of multiple parameters, the proposed method takes advantage of the data-dependent strategies in the pattern recognition procedures. In this method, outliers (superpositions) are excluded prior to classification and MUAP templates are identified by an adaptive density-based clustering procedure. MUAP trains are then identified by a novel density-based classifier that incorporates MUAP shape and discharge time information. MUAP trains are merged by a fuzzy system that incorporates expert human knowledge. Finally, superimpositions are resolved to fill the gaps in the MUAP trains. The proposed decomposition algorithm has been experimentally tested on signals from low-force (<= 30% maximal) isometric contractions of the vastus medialis obliquus, vastus lateralis, biceps femoris long-head and tibialis anterior muscles. Comparison with expert manual decomposition that had been verified using a rigorous statistical analysis showed that the algorithm identified 80% of the total 229 motor unit trains with an accuracy greater than 90%. The algorithm is robust and accurate, and therefore it is a promising new tool for decomposing single-channel multi-unit signals."],["dc.identifier.doi","10.1088/1741-2560/8/6/066015"],["dc.identifier.isi","000297684400028"],["dc.identifier.pmid","22063475"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/21450"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Iop Publishing Ltd"],["dc.relation.issn","1741-2552"],["dc.relation.issn","1741-2560"],["dc.title","Robust decomposition of single-channel intramuscular EMG signals at low force levels"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]