Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation.
Scientific Abstract
Studying the brain in its natural state remains a major challenge for neuroscience. Solving this challenge would not only enable the refinement of cognitive theory, but also provide a better understanding of cognitive function in the type of complex and unpredictable situations that constitute daily life, and which are often disturbed in clinical populations. With mobile EEG, researchers now have access to a tool that can help address these issues. In this paper we present an overview of technical advancements in mobile EEG systems and associated analysis tools, and explore the benefits of this new technology. Using the example of motor imagery (MI) we will examine the translational potential of MI-based neurofeedback training for neurological rehabilitation and applied research.
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Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation.
Scientific Abstract
Studying the brain in its natural state remains a major challenge for neuroscience. Solving this challenge would not only enable the refinement of cognitive theory, but also provide a better understanding of cognitive function in the type of complex and unpredictable situations that constitute daily life, and which are often disturbed in clinical populations. With mobile EEG, researchers now have access to a tool that can help address these issues. In this paper we present an overview of technical advancements in mobile EEG systems and associated analysis tools, and explore the benefits of this new technology. Using the example of motor imagery (MI) we will examine the translational potential of MI-based neurofeedback training for neurological rehabilitation and applied research.
Citation
2014. Int J Psychophysiol, 91(1):10-5.
DOI
10.1016/j.ijpsycho.2013.10.004
Similar content
Paper
Wireless EEG with individualized channel layout enables efficient motor imagery training.
2015. Clin Neurophysiol, 126(4):698-710.
Paper
Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery.
2015. Neuroimage, 114:438-47.
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Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study.
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Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures.
2017. Neurobiol Aging, 49:183-197.