Dataset Information

Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses.

ABSTRACT:

Background

The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfortunately, this choice is still restrained by the reduced knowledge of the dynamics of human efferent signals recorded from the nerves and associated to hand movements.

Findings

To address this issue, in this work we acquired neural efferent activities from healthy subjects performing hand-related tasks using ultrasound-guided microneurography, a minimally invasive technique, which employs needles, inserted percutaneously, to record from nerve fibers. These signals allowed us to identify neural features correlated with force and velocity of finger movements that were used to decode motor intentions. We developed computational models, which confirmed the potential translatability of these results showing how these neural features hold in absence of feedback and when implantable intrafascicular recording, rather than microneurography, is performed.

Conclusions

Our results are a proof of principle that microneurography could be used as a useful tool to assist the development of more effective hand prostheses.

SUBMITTER: Petrini FM

PROVIDER: S-EPMC6454621 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses.

Petrini Francesco M FM Mazzoni Alberto A Rigosa Jacopo J Giambattistelli Federica F Granata Giuseppe G Barra Beatrice B Pampaloni Alessandra A Guglielmelli Eugenio E Zollo Loredana L Capogrosso Marco M Micera Silvestro S Raspopovic Stanisa S

Biomedical engineering online 20190408 1

<h4>Background</h4>The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfortunately, this choice is still restrained by the reduced knowledge of the dynamics of human efferent signals recorded from the nerves and associated to hand movements.<h4>Findings</h4>To addre ...[more]

PMID: 30961620

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Project description:Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training. Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks. Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t(7) = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t(7) = 1.33). Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.

Project description:Introduction:Sensory feedback in hand prostheses is lacking but wished for. Many amputees experience a phantom hand map on their residual forearm. When the phantom hand map is touched, it is experienced as touch on the amputated hand. A non-invasive sensory feedback system, applicable to existing hand prostheses, can transfer somatotopical sensory information via phantom hand map. The aim was to evaluate how forearm amputees experienced a non-invasive sensory feedback system used in daily life over a 4-week period. Methods:This longitudinal cohort study included seven forearm amputees. A non-invasive sensory feedback system was used over 4 weeks. For analysis, a mixed method was used, including quantitative tests (ACMC, proprioceptive pointing task, questionnaire) and interviews. A directed content analysis with predefined categories sensory feedback from the prosthesis, agency, body ownership, performance in activity, and suggestions for improvements was applied. Results:The results from interviews showed that sensory feedback was experienced as a feeling of touch which contributed to an experience of completeness. However, the results from the questionnaire showed that the sense of agency and performance remained unchanged or deteriorated. The ability to feel and manipulate small objects was difficult and a stronger feedback was wished for. Phantom pain was alleviated in four out of five patients. Conclusion:This is the first time a non-invasive sensory feedback system for hand prostheses was implemented in the home environment. The qualitative and quantitative results diverged. The sensory feedback was experienced as a feeling of touch which contributed to a feeling of completeness, linked to body ownership. The qualitative result was not verified in the quantitative measurements. Clinical Trial Registration:Name: Evaluation of a Non-invasive Sensory Feedback System in Hand Prostheses. Date of registration: March 15, 2019. Date the first participant was enrolled: April 1, 2015. ClinicalTrials.gov Identifier: NCT03876405 ORCID ID: https://orcid.org/0000-0002-4140-7478.

Dataset Information

Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses.

Background

Findings

Conclusions

Publications

Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses.

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