Hahne, Janne Mathias

[["dc.bibliographiccitation.artnumber","9638098"],["dc.bibliographiccitation.journal","BioMed Research International"],["dc.contributor.author","Hwang, Han-Jeong"],["dc.contributor.author","Kim, Do-Won"],["dc.contributor.author","Hahne, Janne M."],["dc.contributor.author","Son, Jongsang"],["dc.date.accessioned","2018-11-07T10:28:37Z"],["dc.date.available","2018-11-07T10:28:37Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1155/2017/9638098"],["dc.identifier.isi","000400402400001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14773"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/43463"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Hindawi Ltd"],["dc.relation.issn","2314-6141"],["dc.relation.issn","2314-6133"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Neural Engineering for Rehabilitation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.subtype","letter_note"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Hahne, Janne M."],["dc.contributor.author","Daehne, Sven"],["dc.contributor.author","Hwang, Han-Jeong"],["dc.contributor.author","Mueller, Klaus-Robert"],["dc.contributor.author","Parra, Lucas C."],["dc.date.accessioned","2018-11-07T09:49:30Z"],["dc.date.available","2018-11-07T09:49:30Z"],["dc.date.issued","2015"],["dc.format.extent","1128"],["dc.identifier.doi","10.1109/TNSRE.2015.2497038"],["dc.identifier.isi","000364855300022"],["dc.identifier.pmid","26560083"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35520"],["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","Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control (vol 23, pg 618, 2015)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","269"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","279"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Hahne, Janne M."],["dc.contributor.author","Biessmann, F."],["dc.contributor.author","Jiang, N."],["dc.contributor.author","Rehbaum, Hubertus"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Meinecke, F. C."],["dc.contributor.author","Mueller, K.-R."],["dc.contributor.author","Parra, Lucas C."],["dc.date.accessioned","2018-11-07T09:42:44Z"],["dc.date.available","2018-11-07T09:42:44Z"],["dc.date.issued","2014"],["dc.description.abstract","In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices."],["dc.identifier.doi","10.1109/TNSRE.2014.2305520"],["dc.identifier.isi","000342078300008"],["dc.identifier.pmid","24608685"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34022"],["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","Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","114"],["dc.bibliographiccitation.journal","Frontiers in Neuroscience"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Hahne, Janne M."],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Jiang, Ning"],["dc.contributor.author","Liebetanz, David"],["dc.date.accessioned","2018-11-07T10:16:39Z"],["dc.date.available","2018-11-07T10:16:39Z"],["dc.date.issued","2016"],["dc.description.abstract","Despite several decades of research, electrically powered hand and arm prostheses are still controlled with very simple algorithms that process the surface electromyogram (EMG) of remnant muscles to achieve control of one prosthetic function at a time. More advanced machine learning methods have shown promising results under laboratory conditions. However, limited robustness has largely prevented the transfer of these laboratory advances to clinical applications. In this paper, we introduce a novel percutaneous EMG electrode to be implanted chronically with the aim of improving the reliability of EMG detection in myoelectric control. The proposed electrode requires a minimally invasive procedure for its implantation, similar to a cosmetic micro-dermal implant. Moreover, being percutaneous, it does not require power and data telemetry modules. Four of these electrodes were chronically implanted in the forearm of an able-bodied human volunteer for testing their characteristics. The implants showed significantly lower impedance and greater robustness against mechanical interference than traditional surface EMG electrodes used for myoelectric control. Moreover, the EMG signals detected by the proposed systems allowed more stable control performance across sessions in different days than that achieved with classic EMG electrodes. In conclusion, the proposed implants may be a promising interface for clinically available prostheses."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2016"],["dc.identifier.doi","10.3389/fnins.2016.00114"],["dc.identifier.isi","000373036700002"],["dc.identifier.pmid","27065783"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13226"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/41077"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1662-453X"],["dc.relation.issn","1662-453X"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","A Novel Percutaneous Electrode Implant for Improving Robustness in Advanced Myoelectric 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","eaat3630"],["dc.bibliographiccitation.issue","19"],["dc.bibliographiccitation.journal","Science Robotics"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Hahne, Janne M."],["dc.contributor.author","Schweisfurth, Meike A."],["dc.contributor.author","Koppe, Mario"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2020-12-10T18:36:46Z"],["dc.date.available","2020-12-10T18:36:46Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1126/scirobotics.aat3630"],["dc.identifier.eissn","2470-9476"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76731"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Simultaneous control of multiple functions of bionic hand prostheses: Performance and robustness in end users"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","056028"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Neural Engineering"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Schmalfuss, Leonie"],["dc.contributor.author","Hahne, Janne"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Hewitt, Manuel"],["dc.contributor.author","Kogut, Andreas"],["dc.contributor.author","Doneit, Wolfgang"],["dc.contributor.author","Reischl, Markus"],["dc.contributor.author","Rupp, Rüdiger"],["dc.contributor.author","Liebetanz, David"],["dc.date.accessioned","2020-12-10T18:15:50Z"],["dc.date.available","2020-12-10T18:15:50Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1088/1741-2552/aad727"],["dc.identifier.eissn","1741-2552"],["dc.identifier.issn","1741-2560"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74971"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","A hybrid auricular control system: direct, simultaneous, and proportional myoelectric control of two degrees of freedom in prosthetic hands"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","618"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","627"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Hahne, Janne M."],["dc.contributor.author","Daehne, Sven"],["dc.contributor.author","Hwang, Han-Jeong"],["dc.contributor.author","Mueller, Klaus-Robert"],["dc.contributor.author","Parra, Lucas C."],["dc.date.accessioned","2018-11-07T09:55:18Z"],["dc.date.available","2018-11-07T09:55:18Z"],["dc.date.issued","2015"],["dc.description.abstract","Myoelectric control of a prosthetic hand with more than one degree of freedom (DoF) is challenging, and clinically available techniques require a sequential actuation of the DoFs. Simultaneous and proportional control of multiple DoFs is possible with regression-based approaches allowing for fluent and natural movements. Conventionally, the regressor is calibrated in an open-loop with training based on recorded data and the performance is evaluated subsequently. For individuals with amputation or congenital limb-deficiency who need to (re) learn how to generate suitable muscle contractions, this open-loop process may not be effective. We present a closed-loop real-time learning scheme in which both the user and the machine learn simultaneously to follow a common target. Experiments with ten able-bodied individuals show that this co-adaptive closed-loop learning strategy leads to significant performance improvements compared to a conventional open-loop training paradigm. Importantly, co-adaptive learning allowed two individuals with congenital deficiencies to perform simultaneous 2-D proportional control at levels comparable to the able-bodied individuals, despite having to a learn completely new and unfamiliar mapping from muscle activity to movement trajectories. To our knowledge, this is the first study which investigates man-machine co-adaptation for regression-based myoelectric control. The proposed training strategy has the potential to improve myographic prosthetic control in clinically relevant settings."],["dc.identifier.doi","10.1109/TNSRE.2015.2401134"],["dc.identifier.isi","000357596500010"],["dc.identifier.pmid","25680209"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/36709"],["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","Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0186318"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PloS one"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Hwang, Han-Jeong"],["dc.contributor.author","Hahne, Janne Mathias"],["dc.contributor.author","Müller, Klaus-Robert"],["dc.date.accessioned","2019-07-09T11:44:41Z"],["dc.date.available","2019-07-09T11:44:41Z"],["dc.date.issued","2017"],["dc.description.abstract","There are some practical factors, such as arm position change and donning/doffing, which prevent robust myoelectric control. The objective of this study is to precisely characterize the impacts of the two representative factors on myoelectric controllability in practical control situations, thereby providing useful references that can be potentially used to find better solutions for clinically reliable myoelectric control. To this end, a real-time target acquisition task was performed by fourteen subjects including one individual with congenital upper-limb deficiency, where the impacts of arm position change, donning/doffing and a combination of both factors on control performance was systematically evaluated. The changes in online performance were examined with seven different performance metrics to comprehensively evaluate various aspects of myoelectric controllability. As a result, arm position change significantly affects offline prediction accuracy, but not online control performance due to real-time feedback, thereby showing no significant correlation between offline and online performance. Donning/doffing was still problematic in online control conditions. It was further observed that no benefit was attained when using a control model trained with multiple position data in terms of arm position change, and the degree of electrode shift caused by donning/doffing was not severely associated with the degree of performance loss under practical conditions (around 1 cm electrode shift). Since this study is the first to concurrently investigate the impacts of arm position change and donning/doffing in practical myoelectric control situations, all findings of this study provide new insights into robust myoelectric control with respect to arm position change and donning/doffing."],["dc.identifier.doi","10.1371/journal.pone.0186318"],["dc.identifier.pmid","29095846"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14868"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59068"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.subject.mesh","Adult"],["dc.subject.mesh","Algorithms"],["dc.subject.mesh","Arm"],["dc.subject.mesh","Artificial Limbs"],["dc.subject.mesh","Electromyography"],["dc.subject.mesh","Female"],["dc.subject.mesh","Humans"],["dc.subject.mesh","Male"],["dc.subject.mesh","Middle Aged"],["dc.title","Real-time robustness evaluation of regression based myoelectric control against arm position change and donning/doffing."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0127231"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Kauppi, Jukka-Pekka"],["dc.contributor.author","Hahne, Janne M."],["dc.contributor.author","Mueller, Klaus-Robert"],["dc.contributor.author","Hyvaerinen, Aapo"],["dc.date.accessioned","2018-11-07T09:56:00Z"],["dc.date.available","2018-11-07T09:56:00Z"],["dc.date.issued","2015"],["dc.description.abstract","Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results."],["dc.identifier.doi","10.1371/journal.pone.0127231"],["dc.identifier.isi","000355700700054"],["dc.identifier.pmid","26039100"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11943"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/36873"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation"],["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","122"],["dc.bibliographiccitation.journal","Neurocomputing"],["dc.bibliographiccitation.lastpage","133"],["dc.bibliographiccitation.volume","298"],["dc.contributor.author","Paaßen, Benjamin"],["dc.contributor.author","Schulz, Alexander"],["dc.contributor.author","Hahne, Janne"],["dc.contributor.author","Hammer, Barbara"],["dc.date.accessioned","2020-12-10T15:20:26Z"],["dc.date.available","2020-12-10T15:20:26Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1016/j.neucom.2017.11.072"],["dc.identifier.issn","0925-2312"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72668"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Expectation maximization transfer learning and its application for bionic hand prostheses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]