Corbet, Tiffany and Iturrate, Iñaki and Pereira, Michael and Perdikis, Serafeim and Millán, José del R (2018) Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance. NeuroImage, 176. pp. 268-276. DOI https://doi.org/10.1016/j.neuroimage.2018.04.005
Corbet, Tiffany and Iturrate, Iñaki and Pereira, Michael and Perdikis, Serafeim and Millán, José del R (2018) Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance. NeuroImage, 176. pp. 268-276. DOI https://doi.org/10.1016/j.neuroimage.2018.04.005
Corbet, Tiffany and Iturrate, Iñaki and Pereira, Michael and Perdikis, Serafeim and Millán, José del R (2018) Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance. NeuroImage, 176. pp. 268-276. DOI https://doi.org/10.1016/j.neuroimage.2018.04.005
Abstract
Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable for many subjects. It has been suggested that the usage of somatosensory feedback would be more suitable than standardly used visual feedback to enhance MI brain patterns. However, somatosensory feedback should not interfere with the recorded MI brain pattern. In this study we propose a novel feedback modality to guide subjects during MI based on sensory threshold neuromuscular electrical stimulation (St-NMES). St-NMES depolarizes sensory and motor axons without eliciting any muscular contraction. We hypothesize that St-NMES does not induce detectable ERD brain patterns and fosters MI performance. Twelve novice subjects were included in a cross-over design study. We recorded their EEG, comparing St-NMES with visual feedback during MI or resting tasks. We found that St-NMES not only induced significantly larger desynchronization over sensorimotor areas (p<0.05) but also significantly enhanced MI brain connectivity patterns. Moreover, classification accuracy and stability were significantly higher with St-NMES. Importantly, St-NMES alone did not induce detectable artifacts, but rather the changes in the detected patterns were due to an increased MI performance. Our findings indicate that St-NMES is a promising feedback in order to foster MI performance and cold be used for BMI online applications.
Item Type: | Article |
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Uncontrolled Keywords: | Motor imagery; Kinesthetic imagery; Sensory electrical stimulation; EEG imaging; Brain-machine interface |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 13 Feb 2019 14:13 |
Last Modified: | 30 Oct 2024 17:36 |
URI: | http://repository.essex.ac.uk/id/eprint/24056 |
Available files
Filename: stNMES_CorbetItPePeMi18.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0