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Brain-Computer Interface ResearchA Brain-Computer-Interface to Combat Musculoskeletal Pain

Brain-Computer Interface Research: A Brain-Computer-Interface to Combat Musculoskeletal Pain [Over the past several years, our group has conceived a completely new technological approach toward BCIs aimed at reversing the maladaptive plasticity induced by musculoskeletal pain. The EEG activity patterns of participants with chronic pain (tennis elbow) were differentiated from those of healthy, age and sex matched controls during real-time movement performance. Our results showed a dominance of power in the alpha frequency range only that was significantly correlated with the intensity of pain (visual analogue scale scale—VAS). Based on this novel finding, a neurofeedback system was developed allowing real-time monitoring of alpha power during idle time and movement execution (wrist extensions). Two bars were shown to the patient on a feedback screen—one containing continuous alpha power, the other only alpha power during the preparation phase of movement execution. The goal of the participant was to maintain the alpha power below the initial baseline value during movement execution. Three patients were tested using this system and their pain intensities were monitored. All participants were successful in decreasing their alpha power across days. This was accompanied by a reduction in their perceived pain VAS scores. In summary, we have developed a neurofeedback system for musculoskeletal pain that is capable of providing rapid, accurate and reliable neurofeedback in dynamic conditions, allowing the users to train their brain to reduce the pain.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Brain-Computer Interface ResearchA Brain-Computer-Interface to Combat Musculoskeletal Pain

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2017
ISBN
978-3-319-57131-7
Pages
123 –130
DOI
10.1007/978-3-319-57132-4_10
Publisher site
See Chapter on Publisher Site

Abstract

[Over the past several years, our group has conceived a completely new technological approach toward BCIs aimed at reversing the maladaptive plasticity induced by musculoskeletal pain. The EEG activity patterns of participants with chronic pain (tennis elbow) were differentiated from those of healthy, age and sex matched controls during real-time movement performance. Our results showed a dominance of power in the alpha frequency range only that was significantly correlated with the intensity of pain (visual analogue scale scale—VAS). Based on this novel finding, a neurofeedback system was developed allowing real-time monitoring of alpha power during idle time and movement execution (wrist extensions). Two bars were shown to the patient on a feedback screen—one containing continuous alpha power, the other only alpha power during the preparation phase of movement execution. The goal of the participant was to maintain the alpha power below the initial baseline value during movement execution. Three patients were tested using this system and their pain intensities were monitored. All participants were successful in decreasing their alpha power across days. This was accompanied by a reduction in their perceived pain VAS scores. In summary, we have developed a neurofeedback system for musculoskeletal pain that is capable of providing rapid, accurate and reliable neurofeedback in dynamic conditions, allowing the users to train their brain to reduce the pain.]

Published: Apr 30, 2017

Keywords: Brain computer interface; Musculoskeletal pain; EEG

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