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Complications Associated With Electromyography: A Systematic Review.

American journal of physical medicine & rehabilitation

Cushman DM, Strenn Q, Elmer A, Yang AJ, Onofrei L.
PMID: 31469680
Am J Phys Med Rehabil. 2020 Feb;99(2):149-155. doi: 10.1097/PHM.0000000000001304.

OBJECTIVES: The aims of the study were to systematically review the available literature concerning complications due to electromyography and to review those associated with nerve conduction studies.DESIGN: A systematic review was undertaken of Medline and Cochrane Central Register of...

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Cushman DM, Strenn Q, Elmer A, et al. Complications Associated With Electromyography: A Systematic Review. Am J Phys Med Rehabil. 2020;99(2):149-155doi: 10.1097/PHM.0000000000001304.
Cushman, D. M., Strenn, Q., Elmer, A., Yang, A. J., & Onofrei, L. (2020). Complications Associated With Electromyography: A Systematic Review. American journal of physical medicine & rehabilitation, 99(2), 149-155. https://doi.org/10.1097/PHM.0000000000001304
Cushman, Daniel M, et al. "Complications Associated With Electromyography: A Systematic Review." American journal of physical medicine & rehabilitation vol. 99,2 (2020): 149-155. doi: https://doi.org/10.1097/PHM.0000000000001304
Cushman DM, Strenn Q, Elmer A, Yang AJ, Onofrei L. Complications Associated With Electromyography: A Systematic Review. Am J Phys Med Rehabil. 2020 Feb;99(2):149-155. doi: 10.1097/PHM.0000000000001304. PMID: 31469680.
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CMOS Magnetic Sensors for Wearable Magnetomyography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Heidari H, Zuo S, Krasoulis A, Nazarpour K.
PMID: 30440821
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2116-2119. doi: 10.1109/EMBC.2018.8512723.

Magnetomyography utilizes magnetic sensors to record small magnetic fields produced by the electrical activity of muscles, which also gives rise to the electromyogram (EMG) signal typically recorded with surface electrodes. Detection and recording of these small fields requires sensitive...

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Heidari H, Zuo S, Krasoulis A, et al. CMOS Magnetic Sensors for Wearable Magnetomyography. Annu Int Conf IEEE Eng Med Biol Soc. 2018;2018:2116-2119doi: 10.1109/EMBC.2018.8512723.
Heidari, H., Zuo, S., Krasoulis, A., & Nazarpour, K. (2018). CMOS Magnetic Sensors for Wearable Magnetomyography. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 20182116-2119. https://doi.org/10.1109/EMBC.2018.8512723
Heidari, Hadi, et al. "CMOS Magnetic Sensors for Wearable Magnetomyography." Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference vol. 2018 (2018): 2116-2119. doi: https://doi.org/10.1109/EMBC.2018.8512723
Heidari H, Zuo S, Krasoulis A, Nazarpour K. CMOS Magnetic Sensors for Wearable Magnetomyography. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2116-2119. doi: 10.1109/EMBC.2018.8512723. PMID: 30440821.
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The combination of multiple affective experiences and their impact on valuation judgments.

Cognition & emotion

Efendić E, Drače S, Ric F.
PMID: 31603032
Cogn Emot. 2020 Jun;34(4):684-699. doi: 10.1080/02699931.2019.1675597. Epub 2019 Oct 11.

People's affective experiences can be influenced by multiple informational inputs. It remains unclear however how this occurs? In this paper, we investigate the construction of affective experiences dependent on the varying number of previously presented, affectively-charged, informational inputs. In...

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Efendić E, Drače S, Ric F. The combination of multiple affective experiences and their impact on valuation judgments. Cogn Emot. 2019;34(4):684-699doi: 10.1080/02699931.2019.1675597.
Efendić, E., Drače, S., & Ric, F. (2020). The combination of multiple affective experiences and their impact on valuation judgments. Cognition & emotion, 34(4), 684-699. https://doi.org/10.1080/02699931.2019.1675597
Efendić, Emir, et al. "The combination of multiple affective experiences and their impact on valuation judgments." Cognition & emotion vol. 34,4 (2020): 684-699. doi: https://doi.org/10.1080/02699931.2019.1675597
Efendić E, Drače S, Ric F. The combination of multiple affective experiences and their impact on valuation judgments. Cogn Emot. 2020 Jun;34(4):684-699. doi: 10.1080/02699931.2019.1675597. Epub 2019 Oct 11. PMID: 31603032.
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An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

Triwiyanto T, Pawana IPA, Purnomo MH.
PMID: 32634104
IEEE Trans Neural Syst Rehabil Eng. 2020 Jul;28(7):1678-1688. doi: 10.1109/TNSRE.2020.2999505.

High accuracy in pattern recognition based on electromyography(EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre-processing based on the convolution neural network (CNN) algorithm for classifying ten...

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Triwiyanto T, Pawana IPA, Purnomo MH. An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter. IEEE Trans Neural Syst Rehabil Eng. 2020;28(7):1678-1688doi: 10.1109/TNSRE.2020.2999505.
Triwiyanto, T., Pawana, I. P. A., & Purnomo, M. H. (2020). An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 28(7), 1678-1688. https://doi.org/10.1109/TNSRE.2020.2999505
Triwiyanto, Triwiyanto, et al. "An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter." IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society vol. 28,7 (2020): 1678-1688. doi: https://doi.org/10.1109/TNSRE.2020.2999505
Triwiyanto T, Pawana IPA, Purnomo MH. An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter. IEEE Trans Neural Syst Rehabil Eng. 2020 Jul;28(7):1678-1688. doi: 10.1109/TNSRE.2020.2999505. PMID: 32634104.
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A method for quantifying electromyograms.

Journal of biomechanics

Beach J, Gorniak GC, Gans C.
PMID: 7142227
J Biomech. 1982;15(8):611-7. doi: 10.1016/0021-9290(82)90072-0.

No abstract available.

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Beach J, Gorniak GC, Gans C. A method for quantifying electromyograms. J Biomech. 1982;15(8):611-7doi: 10.1016/0021-9290(82)90072-0.
Beach, J., Gorniak, G. C., & Gans, C. (1982). A method for quantifying electromyograms. Journal of biomechanics, 15(8), 611-7. https://doi.org/10.1016/0021-9290(82)90072-0
Beach, J, et al. "A method for quantifying electromyograms." Journal of biomechanics vol. 15,8 (1982): 611-7. doi: https://doi.org/10.1016/0021-9290(82)90072-0
Beach J, Gorniak GC, Gans C. A method for quantifying electromyograms. J Biomech. 1982;15(8):611-7. doi: 10.1016/0021-9290(82)90072-0. PMID: 7142227.
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A Multimodal Adaptive Wireless Control Interface for People With Upper-Body Disabilities.

IEEE transactions on biomedical circuits and systems

Fall CL, Quevillon F, Blouin M, Latour S, Campeau-Lecours A, Gosselin C, Gosselin B.
PMID: 29877820
IEEE Trans Biomed Circuits Syst. 2018 Jun;12(3):564-575. doi: 10.1109/TBCAS.2018.2810256.

This paper describes a multimodal body-machine interface (BoMI) to help individuals with upper-limb disabilities using advanced assistive technologies, such as robotic arms. The proposed system uses a wearable and wireless body sensor network (WBSN) supporting up to six sensor...

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Fall CL, Quevillon F, Blouin M, et al. A Multimodal Adaptive Wireless Control Interface for People With Upper-Body Disabilities. IEEE Trans Biomed Circuits Syst. 2018;12(3):564-575doi: 10.1109/TBCAS.2018.2810256.
Fall, C. L., Quevillon, F., Blouin, M., Latour, S., Campeau-Lecours, A., Gosselin, C., & Gosselin, B. (2018). A Multimodal Adaptive Wireless Control Interface for People With Upper-Body Disabilities. IEEE transactions on biomedical circuits and systems, 12(3), 564-575. https://doi.org/10.1109/TBCAS.2018.2810256
Fall, Cheikh Latyr, et al. "A Multimodal Adaptive Wireless Control Interface for People With Upper-Body Disabilities." IEEE transactions on biomedical circuits and systems vol. 12,3 (2018): 564-575. doi: https://doi.org/10.1109/TBCAS.2018.2810256
Fall CL, Quevillon F, Blouin M, Latour S, Campeau-Lecours A, Gosselin C, Gosselin B. A Multimodal Adaptive Wireless Control Interface for People With Upper-Body Disabilities. IEEE Trans Biomed Circuits Syst. 2018 Jun;12(3):564-575. doi: 10.1109/TBCAS.2018.2810256. PMID: 29877820.
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Showing 145 to 150 of 150 entries