Farina, Dario

[["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.artnumber","25"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Lorrain, Thomas"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T08:56:14Z"],["dc.date.available","2018-11-07T08:56:14Z"],["dc.date.issued","2011"],["dc.description.abstract","Background: For high usability, myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set in the performance of several pattern recognition algorithms during dynamic contractions. Methods: A 9 class experiment was designed involving both static and dynamic situations. The performance of various feature extraction methods and classifiers was evaluated in terms of classification accuracy. Results: It is shown that, combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions provide relatively high classification accuracy also on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested significantly improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time domain features provide results comparable to more complex classification methods of wavelet features. Conclusions: Non-stationary surface EMG signals recorded during dynamic contractions can be accurately classified for the control of multi-function prostheses."],["dc.identifier.doi","10.1186/1743-0003-8-25"],["dc.identifier.isi","000291589200001"],["dc.identifier.pmid","21554700"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6371"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23090"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1743-0003"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["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","681"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","IEEE Transactions on Biomedical Engineering"],["dc.bibliographiccitation.lastpage","688"],["dc.bibliographiccitation.volume","58"],["dc.contributor.author","Nielsen, Johnny L. G."],["dc.contributor.author","Holmgaard, Steffen"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Englehart, Kevin B."],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Parker, Phil A."],["dc.date.accessioned","2018-11-07T08:58:50Z"],["dc.date.available","2018-11-07T08:58:50Z"],["dc.date.issued","2011"],["dc.description.abstract","This study presents a novel method for associating features of the surface electromyogram (EMG) recorded from one upper limb to the force produced by the contralateral limb. Bilateral-mirrored contractions from ten able-bodied subjects were recorded along with isometric forces in multiple degrees of freedom (DOF) from the right wrist. An artificial neural network was trained to provide force estimation. Combinations of processing parameters were evaluated and an estimation algorithm allowing high accuracy from relatively short signal epochs (100 ms) was proposed. The estimation performance when using surface EMG from the contralateral limb was 0.90 +/- 0.02 for the able-bodied subjects. In comparison, the estimation performance for one subject with congenital malformation of the left forearm was 0.72 which, albeit lower than for able-bodied subjects, is still comparable to or better than previously reported results. The proposed method requires only the measured forces from one limb, such as in the case of unilateral amputees and has thus the potential to be used in clinical settings for intuitive, simultaneous control of multiple DOFs in myoelectric prostheses."],["dc.identifier.doi","10.1109/TBME.2010.2068298"],["dc.identifier.isi","000287661900024"],["dc.identifier.pmid","20729161"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23738"],["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","Simultaneous and Proportional Force Estimation for Multifunction Myoelectric Prostheses Using Mirrored Bilateral Training"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","550"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","IEEE Transactions on Biomedical Engineering"],["dc.bibliographiccitation.lastpage","556"],["dc.bibliographiccitation.volume","58"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T08:58:50Z"],["dc.date.available","2018-11-07T08:58:50Z"],["dc.date.issued","2011"],["dc.description.abstract","An extension of the blind source separation technique based on the second-order blind identification (SOBI) approach is presented to separate mixtures of delayed sources. When the delay is small such that the first-order Taylor approximation holds, the delayed mixture is transformed as the mixture of the original sources and their derivatives. Two algorithms are proposed for the rotation step that recovers the extended source vector (original sources and the corresponding derivatives). The first approach is based on the odd symmetry of the derivative of the autocorrelation function; and the second method identifies the locations of single auto terms in the optimized time-scale plane. A simulation analysis was conducted to evaluate the performance of the proposed algorithms. The results showed that the proposed methods substantially improved the performance of SOBI and its extension in the time-scale plane when the sources presented delays in the mixtures. In addition, the proposed algorithms were applied representatively to experimental multichannel surface electromyographic signals to identify motor unit action potential trains from the interference signal. The performance of the proposed methods was superior to previous methods also in this representative application. In conclusion, extensions of the SOBI approach of source separation have been proposed for the case of sources being delayed in the mixtures. These techniques were proven superior to previous approaches."],["dc.description.sponsorship","EU [FP7-ICT-2007-2]"],["dc.identifier.doi","10.1109/TBME.2010.2084999"],["dc.identifier.isi","000287661900010"],["dc.identifier.pmid","20934944"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23737"],["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","Covariance and Time-Scale Methods for Blind Separation of Delayed Sources"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","47"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Kapelner, Tamás"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Negro, Francesco"],["dc.contributor.author","Aszmann, Oskar C."],["dc.contributor.author","Principe, Jose"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:51:40Z"],["dc.date.available","2019-07-09T11:51:40Z"],["dc.date.issued","2019"],["dc.description.abstract","BACKGROUND: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. METHODS: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. RESULTS: The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). CONCLUSIONS: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control."],["dc.identifier.doi","10.1186/s12984-019-0516-x"],["dc.identifier.pmid","30953528"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16168"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59987"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/267888/EU//DEMOVE"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/737570/EU//INTERSPINE"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/702491/EU//NeuralCon"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","110"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Lorrain, Thomas"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:37:45Z"],["dc.date.available","2018-11-07T09:37:45Z"],["dc.date.issued","2014"],["dc.description.abstract","Background: Current clinical myoelectric systems provide unnatural prosthesis control, with limited functionality. In this study, we propose a proportional state-based control method, which allows switching between functions in a more natural and intuitive way than the traditional co-contraction switch method. Methods: We validated the ability of the proposed system to provide precise control in both position and velocity modes. Two tests were performed with online visual feedback, involving target reaching and direct force control in grasping. The performance of the system was evaluated both on a subject with limb deficiency and in 9 intact-limbed subjects, controlling two degrees of freedom (DoF) of the hand and wrist. Results: The system allowed completion of the tasks involving 1-DoF with task completion rate >96% and of those involving 2-DoF with completion rate >91%. When compared with the clinical/industrial state-of-the-art approach and with a classic pattern recognition approach, the proposed method significantly improved the performance in the 2-DoF tasks. The completion rate in grasping force control was >97% on average. Conclusions: These results indicate that, using the proposed system, subjects were successfully able to operate two DoFs, and to achieve precise force control in grasping. Thus, the proposed state-based method could be a suitable alternative for commercial myoelectric devices, providing reliable and intuitive control of two DoFs."],["dc.description.sponsorship","European Commission [251555, 280778]"],["dc.identifier.doi","10.1186/1743-0003-11-110"],["dc.identifier.isi","000339361000001"],["dc.identifier.pmid","25012766"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32906"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1743-0003"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","A state-based, proportional myoelectric control method: online validation and comparison with the clinical state-of-the-art"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","507"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Medical & Biological Engineering & Computing"],["dc.bibliographiccitation.lastpage","512"],["dc.bibliographiccitation.volume","51"],["dc.contributor.author","Niazi, Imran Khan"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Jochumsen, Mads"],["dc.contributor.author","Nielsen, Jorgen Feldbaek"],["dc.contributor.author","Dremstrup, Kim"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:25:32Z"],["dc.date.available","2018-11-07T09:25:32Z"],["dc.date.issued","2013"],["dc.description.abstract","To allow a routinely use of brain-computer interfaces (BCI), there is a need to reduce or completely eliminate the time-consuming part of the individualized training of the user. In this study, we investigate the possibility of avoiding the individual training phase in the detection of movement intention in asynchronous BCIs based on movement-related cortical potential (MRCP). EEG signals were recorded during ballistic ankle dorsiflexions executed (ME) or imagined (MI) by 20 healthy subjects, and attempted by five stroke subjects. These recordings were used to identify a template (as average over all subjects) for the initial negative phase of the MRCPs, after the application of an optimized spatial filtering used for pre-processing. Using this template, the detection accuracy (mean +/- A SD) calculated as true positive rate (estimated with leave-one-out procedure) for ME was 69 +/- A 21 and 58 +/- A 11 % on single trial basis for healthy and stroke subjects, respectively. This performance was similar to that obtained using an individual template for each subject, which led to accuracies of 71 +/- A 6 and 55 +/- A 12 % for healthy and stroke subjects, respectively. The detection accuracy for the MI data was 65 +/- A 22 % with the average template and 60 +/- A 13 % with the individual template. These results indicate the possibility of detecting movement intention without an individual training phase and without a significant loss in performance."],["dc.identifier.doi","10.1007/s11517-012-1018-1"],["dc.identifier.isi","000317844500003"],["dc.identifier.pmid","23283643"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/30089"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.publisher.place","Heidelberg"],["dc.relation.issn","0140-0118"],["dc.title","Detection of movement-related cortical potentials based on subject-independent training"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1333"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","1341"],["dc.bibliographiccitation.volume","24"],["dc.contributor.author","Hofmann, David"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Vujaklija, Ivan"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T10:05:01Z"],["dc.date.available","2018-11-07T10:05:01Z"],["dc.date.issued","2016"],["dc.description.abstract","The amplitude of the surface EMG (sEMG) is commonly estimated by rectification or other nonlinear transformations, followed by smoothing (low-pass linear filtering). Although computationally efficient, this approach leads to an estimation accuracy with a limited theoretical signal-to-noise ratio (SNR). Since sEMG amplitude is one of the most relevant features for myoelectric control, its estimate has become one of the limiting factors for the performance of myoelectric control applications, such as powered prostheses. In this study, we present a recursive nonlinear estimator of sEMG amplitude based on Bayesian filtering. Furthermore, we validate the advantage of the proposed Bayesian filter over the conventional linear filters through an online simultaneous and proportional control (SPC) task, performed by eight able-bodied subjects and three below-elbow limb deficient subjects. The results demonstrated that the proposed Bayesian filter provides significantly more accurate SPC, particularly for the patients, when compared with conventional linear filters. This result presents a major step toward accurate prosthetic control for advanced multi-function prostheses."],["dc.identifier.doi","10.1109/TNSRE.2015.2501979"],["dc.identifier.isi","000390559600007"],["dc.identifier.pmid","26600161"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14165"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/38814"],["dc.notes.intern","Merged from goescholar"],["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.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Bayesian Filtering of Surface EMG for Accurate Simultaneous and Proportional Prosthetic Control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1167"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Biomedical Engineering"],["dc.bibliographiccitation.lastpage","1176"],["dc.bibliographiccitation.volume","61"],["dc.contributor.author","Amsuess, Sebastian"],["dc.contributor.author","Goebel, Peter M."],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Paredes, Liliana P."],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:41:33Z"],["dc.date.available","2018-11-07T09:41:33Z"],["dc.date.issued","2014"],["dc.description.abstract","Pattern recognition methods for classifying user motion intent based on surface electromyography developed by research groups in well-controlled laboratory conditions are not yet clinically viable for upper limb prosthesis control, due to their limited robustness in users' real-life situations. To address this problem, a novel postprocessing algorithm, aiming to detect and remove misclassifications of a pattern recognition system of fore-arm and hand motions, is proposed. Using the maximum likelihood calculated by a classifier and the mean global muscle activity of the forearm, an artificial neural network was trained to detect potentially erroneous classification decisions. This system was compared to four previously proposed classification postprocessing methods, in both able-bodied and amputee subjects. Various nonstationarities were included in the experimental protocol to account for challenges posed in real-life settings, such as different contraction levels, static and dynamic motion phases, and effects induced by day-to-day transfers, such as electrode shifts, impedance changes, and psychometric user variability. The improvement in classification accuracy with respect to the unprocessed classifier ranged from 4.8% to 31.6%, depending on the scenarios investigated. The system significantly reduced misclassifications to wrong active classes and is thus a promising approach for improving the robustness of hand prosthesis controllability."],["dc.identifier.doi","10.1109/TBME.2013.2296274"],["dc.identifier.isi","000337739300015"],["dc.identifier.pmid","24658241"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33759"],["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","Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]