| Title |
Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation
|
|---|---|
| Published in |
Current Physical Medicine and Rehabilitation Reports, April 2014
|
| DOI | 10.1007/s40141-014-0051-4 |
| Pubmed ID | |
| Authors |
Anusha Venkatakrishnan, Gerard E. Francisco, Jose L. Contreras-Vidal |
| Abstract |
Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain-machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neu-rorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 1% |
| Czechia | 1 | <1% |
| Taiwan | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Spain | 1 | <1% |
| Japan | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 146 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 34 | 22% |
| Student > Master | 22 | 14% |
| Student > Bachelor | 16 | 10% |
| Researcher | 14 | 9% |
| Student > Doctoral Student | 9 | 6% |
| Other | 29 | 19% |
| Unknown | 30 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 65 | 42% |
| Medicine and Dentistry | 13 | 8% |
| Computer Science | 10 | 6% |
| Neuroscience | 10 | 6% |
| Nursing and Health Professions | 5 | 3% |
| Other | 15 | 10% |
| Unknown | 36 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 July 2014.
All research outputs
#17,722,431
of 22,757,541 outputs
Outputs from Current Physical Medicine and Rehabilitation Reports
#101
of 154 outputs
Outputs of similar age
#156,478
of 226,944 outputs
Outputs of similar age from Current Physical Medicine and Rehabilitation Reports
#5
of 6 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 154 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 226,944 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.