title   
  

Electroencephalographic identifiers of motor adaptation learning

Özdenizci, Ozan and Yalçın, Mustafa and Erdoğan, Ahmetcan and Patoğlu, Volkan and Grosse-Wentrup, Moritz and Çetin, Müjdat (2017) Electroencephalographic identifiers of motor adaptation learning. Journal of Neural Engineering, 14 (4). ISSN 1741-2560 (Print) 1741-2552 (Online)

[img]PDF - Registered users only - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
2416Kb

Official URL: http://dx.doi.org/10.1088/1741-2552/aa6abd

Abstract

Objective. Recent brain-computer interface (BCI) assisted stroke rehabilitation protocols tend to focus on sensorimotor activity of the brain. Relying on evidence claiming that a variety of brain rhythms beyond sensorimotor areas are related to the extent of motor deficits, we propose to identify neural correlates of motor learning beyond sensorimotor areas spatially and spectrally for further use in novel BCI-assisted neurorehabilitation settings. Approach. Electroencephalographic (EEG) data were recorded from healthy subjects participating in a physical force-field adaptation task involving reaching movements through a robotic handle. EEG activity recorded during rest prior to the experiment and during pre-trial movement preparation was used as features to predict motor adaptation learning performance across subjects. Main results. Subjects learned to perform straight movements under the force-field at different adaptation rates. Both resting-state and pre-trial EEG features were predictive of individual adaptation rates with relevance of a broad network of beta activity. Beyond sensorimotor regions, a parieto-occipital cortical component observed across subjects was involved strongly in predictions and a fronto-parietal cortical component showed significant decrease in pre-trial beta-powers for users with higher adaptation rates and increase in pre-trial beta-powers for users with lower adaptation rates. Significance. Including sensorimotor areas, a large-scale network of beta activity is presented as predictive of motor learning. Strength of resting-state parieto-occipital beta activity or pre-trial fronto-parietal beta activity can be considered in BCI-assisted stroke rehabilitation protocols with neurofeedback training or volitional control of neural activity for brain-robot interfaces to induce plasticity.

Item Type:Article
Uncontrolled Keywords:motor learning; electroencephalogram; resting-state; pre-trial; brain-computer interface
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:33798
Deposited By:Müjdat Çetin
Deposited On:10 Sep 2017 16:23
Last Modified:10 Sep 2017 16:23

Repository Staff Only: item control page