Zobrazeno 1 - 10
of 108
pro vyhledávání: '"Michael Houston"'
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 1045-1054 (2024)
The coupled analysis of corticomuscular function based on physiological electrical signals can identify differences in causal relationships between electroencephalogram (EEG) and surface electromyogram (sEMG) in different motor states. The existing m
Externí odkaz:
https://doaj.org/article/961bb0ad4b904f03bbe9dade2cbd257f
Autor:
Hao Meng, Michael Houston, Nicholas Dias, Chen Guo, Gerard Francisco, Yingchun Zhang, Sheng Li
Publikováno v:
Biomedicines, Vol 12, Iss 11, p 2635 (2024)
Previous studies have shown that beta-band transcranial alternating current stimulation (tACS) applied at the M1 hotspot can modulate corticospinal excitability. However, it remains controversial whether tACS can influence motor unit activities at th
Externí odkaz:
https://doaj.org/article/dbdbb5a00eff4630a3109c18cd94122b
Publikováno v:
Brain Sciences, Vol 14, Iss 4, p 322 (2024)
Introduction: Stroke survivors often have motor impairments and related functional deficits. Transcranial Electrical Stimulation (tES) is a rapidly evolving field that offers a wide range of capabilities for modulating brain function, and it is safe
Externí odkaz:
https://doaj.org/article/e1222eeb866f4126a5f8407180464738
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 4831-4838 (2023)
This study aims to characterize motor unit (MU) features associated with muscle fatigue, using high-density surface electromyography (HD-sEMG). The same MUs recruited before/after, and during muscle fatigue were identified for analysis. The surface l
Externí odkaz:
https://doaj.org/article/e240438f392d48b1adcfae11fe091dcb
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 4635-4643 (2023)
In musculoskeletal systems, describing accurately the coupling direction and intensity between physiological electrical signals is crucial. The maximum information coefficient (MIC) can effectively quantify the coupling strength, especially for short
Externí odkaz:
https://doaj.org/article/6ae60befbe314571b53bbb0ce38e5f22
Autor:
Yun Zheng, Yuliang Ma, Yang Liu, Michael Houston, Chen Guo, Qidao Lian, Sheng Li, Ping Zhou, Yingchun Zhang
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 3641-3651 (2023)
Objective- This study aims to develop a novel framework for high-density surface electromyography (HD-sEMG) signal decomposition with superior decomposition yield and accuracy, especially for low-energy MUs. Methods- An iterative convolution kernel c
Externí odkaz:
https://doaj.org/article/5385d375d9e4464996de812f4fa16b52
Autor:
Zhuyao Fan, Xugang Xi, Yunyuan Gao, Ting Wang, Feng Fang, Michael Houston, Yingchun Zhang, Lihua Li, Zhong Lu
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 2101-2110 (2023)
Motor imagery (MI) electroencephalogram (EEG) signals have an important role in brain-computer interface (BCI) research. However, effectively decoding these signals remains a problem to be solved. Traditional EEG signal decoding algorithms rely on pa
Externí odkaz:
https://doaj.org/article/a5c6c6ad534d4119afcb612760170b50
Autor:
Hongming Liu, Yunyuan Gao, Wei Huang, Rihui Li, Michael Houston, Julia S. Benoit, Jinsook Roh, Yingchun Zhang
Publikováno v:
Mathematical Biosciences and Engineering, Vol 19, Iss 5, Pp 4506-4525 (2022)
Muscle coordination and motor function of stroke patients are weakened by stroke-related motor impairments. Our earlier studies have determined alterations in inter-muscular coordination patterns (muscle synergies). However, the functional connectivi
Externí odkaz:
https://doaj.org/article/702aac0890bf4803a8d8998a9d876ee9
Autor:
Yi Tao, Weiwei Xu, Guangming Wang, Ziwen Yuan, Maode Wang, Michael Houston, Yingchun Zhang, Badong Chen, Xiangguo Yan, Gang Wang
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 30, Pp 2754-2763 (2022)
Brain-computer interface (BCI) is a technology that connects the human brain and external devices. Many studies have shown the possibility of using it to restore motor control in stroke patients. One specific challenge of such BCI is that the classif
Externí odkaz:
https://doaj.org/article/364d3019fd9f4cbdbc75abb93c016d6a
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 72:1-10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a503c316f5a60a4719c95e3f6bbcbce3
https://doi.org/10.1109/tim.2023.3276509
https://doi.org/10.1109/tim.2023.3276509