On the intersubject generalization ability in extracting kinematic information from afferent nervous signals
Abstract
In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in te…
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Recently developed algorithms using the neuro-fuzzy approach allow extraction of useful kinematic information mimicking actual trajectories.
“Recently developed algorithms using this approach allow the extraction of useful kinematic information mimicking the actual trajectories (Cavallaro et al., 2003)”
From A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems (2005) · cites this paper as Cavallaro et al., 2003
Recorded ENG using cuff electrodes allowed prediction of ankle movements in an animal model.
“Recorded ENG signals using cuff electrodes allowed the prediction of ankle movements in an animal model (Micera et al., 2001a; Jensen et al., 2002; Cavallaro et al., 2003)”
From A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems (2005) · cites this paper as Cavallaro et al., 2003
Cites
Micera, S., Jensen, W., Sepulveda, F., Riso, R. R., & Sinkjaer, T. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.930903 · Cited by 58 · see citing works
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Navarro, X., Krueger, T. B., Lago, N., Micera, S., Stieglitz, T., & Dario, P. (2005) · Journal of the Peripheral Nervous System · doi:10.1111/j.1085-9489.2005.10303.x · Cited by 845 · see citing works
“Recorded ENG signals using cuff electrodes allowed the prediction of ankle movements in an animal model (Micera et al., 2001a; Jensen et al., 2002; Cavallaro et al., 2003) with the idea of using this information for the closed-loop control of standing in paraplegic subjects.”
BackgroundMethodSelf-citationDesign of a cybernetic hand for perception and action
Carrozza, M. C., Cappiello, G., Micera, S., Edin, B. B., Beccai, L., & Cipriani, C. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0124-2 · Cited by 339 · see citing works
“2006), as well as toward the development of novel signal processing algorithms (Micera et al. 2001; Cavallaro et al. 2003; Tesfayesus and Durand 2006).”
BackgroundSelf-citationMicera, S., Navarro, X., Carpaneto, J., Citi, L., Tonet, O., Rossini, P. M., Carrozza, M. C., Hoffmann, K. P., Vivo, M., Yoshida, K., et al. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.2006207 · Cited by 120 · see citing works
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“…limits can be partly overcome by using multi-site cuff electrodes (Yoo and Durand, 2005) and advanced processing techniques (Micera et al., 2001; Cavallaro et al., 2003; Lin et al., 2007; Tesfayesus and Durand, 2007), more selective PNS interfaces may be necessary to access more specific…”
Self-citationHybrid Bionic Systems for the Replacement of Hand Function
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Self-citationChapter 2 Bidirectional Interfaces with the Peripheral Nervous System
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Self-citationMotor-commands decoding using peripheral nerve signals: a review
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“A wide variety of algorithms have been tested for ENG and EMG signals, including support vector machines (SVM) [41, 63, 64], artificial neural networks [65], Kalman filters [89], genetic algorithms [150], and Bayesian methods.”
MethodExtracting Fuzzy Rules From Polysomnographic Recordings for Infant Sleep Classification
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Neural signal recording and processing in somatic neuroprosthetic applications. A review
Raspopović, S., Cimolato, A., Panarese, A., Vallone, F., del Valle, J., Micera, S., & Navarro, X. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108653 · Cited by 51 · see citing works
“Neuro-Fuzzy Networks and Genetic Algorithms, or a combination of the two, can be used in order to automate the procedure (Cavallaro et al., 2003).”
MethodSelf-citationSahab, N., & Hagras, H. (2011) · International Journal of Computers Communications & Control · doi:10.15837/ijccc.2011.3.2133 · Cited by 45
A sensorized thumb for force closed-loop control of hand neuroprostheses
Carpaneto, J., Micera, S., Zaccone, F., Vecchi, F., & Dario, P. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.819938 · Cited by 28 · see citing works
“The use of natural sensors is very appealing and might represent the future of FNS-based restoration systems as demonstrated by the increasing number of centers involved in similar researches [18]–[24].”
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Model-based Bayesian signal extraction algorithm for peripheral nerves
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On the development of a biomechatronic system to record tendon sliding movements
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“Therefore, the use of artificial sensors [4]–[6] or the extraction of information from natural sensors by processing electroneurographic signals [7]–[10] is necessary.”
BackgroundSelf-citationA fuzzy MCDM method to select the best company based on financial report analysis
Kung, J., Chuang, T.-N., & Ky, C. M. (2011) · IEEE International Conference on Fuzzy Systems · doi:10.1109/fuzzy.2011.6007679 · Cited by 12
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Song, K., Park, S., Hwang, D., & Youn, I. (2018) · Annals of Biomedical Engineering · doi:10.1007/s10439-018-02181-1 · Cited by 11 · see citing works
A hybrid approach to modeling input variables in non-singleton type-2 Fuzzy Logic Systems
Sahab, N., & Hagras, H. (2010) · UK Workshop on Computational Intelligence · doi:10.1109/ukci.2010.5625602 · Cited by 11
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MethodModel-based ankle joint angle tracing by cuff electrode recordings of peroneal and tibial nerves
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“The performance of joint angle trajectory tracing by our model was as precise as those in other studies [2, 14, 18].”
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“heel strike) [42, 43] or tracking of movement trajectories [44, 45 ].”
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Self-citationYoshida, K., Kurstjens, M., Citi, L., Koch, K. P., & Micera, S. (2007) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2007.4428525 · Cited by 5 · see citing works
“using, for example, signals from natural sensors [6-8].”
Self-citationClassification of Afferent Signals Recorded with a Single Cuff Electrode
Choi, C.-M., Carpaneto, J., Lago, N., Kim, J., Dario, P., Navarro, X., & Micera, S. (2007) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2007.4352807 · Cited by 6 · see citing works
Self-citationDevelopment and Performance Evaluation of a Neural Signal-based Assistive Computer Interface
Choi, C.-M., Han, H., Kim, C., & Kim, J. (2007) · IEEE International Symposium on Robot and Human Interactive Communication · doi:10.1109/roman.2007.4415219 · Cited by 6 · see citing works
“Signals at the level of the peripheral nervous system, which result in body motion, can be detected by an electroneurogram (ENG) [5] and an electromyogram (EMG) [6].”
Technical Advancement on Various Bio-signal Controlled Arm- A review
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Efficient peripheral nerve firing characterisation through massive feature extraction
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“Given these constraints on data quality, decoding is most often based on the amplitude of the rectified and integrated signal [6], [7] or the power (as square of amplitudes from a Fourier spectrum) in a certain frequency band [8].”
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A phantom axon setup for validating models of action potential recordings
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“Signals can be acquired through cuff [5] or intrafascicular electrodes [9, 27] with various geometries and numbers of contacts.”
Lin, C.-C. K., Ju, M.-S., & Chan, C.-C. (2010) · 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology · doi:10.1109/iembs.2010.5627926
“The learning capability of the DNSFLS was later improved by incorporating a genetic learning algorithm [ 2 ].”
Method
- Authors
- Cavallaro, E., Micera, S., Dario, P., Jensen, W., & Sinkjaer, T.
- Affiliations
- Scuola Superiore Sant'Anna (IT)
- Aalborg University (DK)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2003-09
- Volume
- 50
- Issue
- 9
- Pages
- 1063-1073
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2112801074
- Cited by
- 44
- References
- 32
- Citations by year
- 2024: 1 · 2023: 1 · 2020: 2 · 2019: 1 · 2018: 3 · 2017: 1 · 2016: 2 · 2015: 2
- FWCI
- 2.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- neural prostheses, electromyography, muscle afferents, genetic algorithm, Takagi-Sugeno-Kang fuzzy systems, feedback control, cuff electrodes, animal models
- MeSH terms
- Action Potentials, Afferent Pathways, Algorithms, Animals, Ankle Joint, Electrodes, Implanted, Electrodiagnosis, Feedback, Female, Movement, Peroneal Nerve, Physical Stimulation, Rabbits, Rotation, Sensitivity and Specificity, Tibial Nerve, Reproducibility of Results, Neural Networks, Computer, Fuzzy Logic, Muscle, Skeletal
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