Closed-Loop Control of Myoelectric Prostheses With Electrotactile Feedback: Influence of Stimulation Artifact and Blanking
Abstract
Electrocutaneous stimulation is a promising approach to provide sensory feedback to amputees, and thus close the loop in upper limb prosthetic systems. However, the stimulation introduces artifacts in the recorded electromyographic (EMG) signals, which may be detrimental for the control of myoelectric prostheses. In this study, artifact blanking with three data segmentation approaches was investigated as a simple method to restore the performance of pattern recognition in prosthesis control (eight motions) when EMG signals are corrupted by stimulation artifacts. The methods were tested over a range of stimulation conditions and using four feature sets, comprising both time and frequency domain features. The results demonstrated that when stimulation artifacts were present, the classification performance improved with blanking in all tested conditions. In some cases, the classification performance with blanking was at the level of the benchmark (artifact-free data). The greatest pulse duration and frequency that allowed a full performance recovery were 400 μs and 150 Hz, respectively. These results show that artifact blanking can be used as a practical solution to eliminate the negative influence of the stimulation artifact on EMG pattern classification in a broad range of conditions, thus allowing to close the loop in myoelectric prostheses using electrotactile feedback.
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Selective sampling methods have been developed to overcome the long discontinuity in recorded data, which synchronise the ADC with stimulation artifacts.
“To overcome the long discontinuity in recorded data, selective sampling methods have been developed, which synchronise the ADC with stimulation artifacts [192].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [192]
A hybrid approach is often adopted that combines multiple structures for effective artifact detection and cancellation while adhering to the system power and area constraints.
“A hybrid approach is often adopted that combines multiple structures for effective artifact detection and cancellation while adhering to the system power and area constraints [152], [192], [193].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [192]
Digital processing techniques such as reconstruction of the biopotentials use interpolation methods that can be applied in parallel to signal blanking to fill in the blocking phases of the synchronised sample-and-hold process.
“Digital processing techniques such as reconstruction of the biopotentials use interpolation methods that can be applied in parallel to signal blanking to fill in the blocking phases of the synchronised sample-and-hold process [192], [195].”
From Active Neural Interface Circuits and Systems for Selective Control of Peripheral Nerves: A Review (2024) · cites this paper as [192]
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References 27 · Cited by 64
- Authors
- Hartmann, C., Dosen, S., Amsuess, S., & Farina, D.
- Affiliations
- Universitätsmedizin Göttingen (DE)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2015-09
- Volume
- 23
- Issue
- 5
- Pages
- 807-816
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2059074146
- Cited by
- 64
- References
- 27
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 6 · 2023: 6 · 2022: 5 · 2021: 5 · 2020: 7 · 2019: 4 · 2018: 5 · 2017: 8 · 2016: 7 · 2015: 2 · 2014: 1
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- GRO (submittedVersion)
- Funders
- People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (286208)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- myoelectric prosthesis, electrotactile feedback, stimulation artifacts, EMG signal classification, upper limb prosthesis control, feedback control, time and frequency domain features
- MeSH terms
- Adult, Algorithms, Electromyography, Electric Stimulation Therapy, Female, Humans, Male, Pattern Recognition, Automated, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Touch, Reproducibility of Results, Artifacts, Muscle, Skeletal, Feedback, Sensory
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