MSc. Thesis Defense:Ozan Özdenizci
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  • MSc. Thesis Defense:Ozan Özdenizci

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IDENTIFYING NEURAL CORRELATES OF MOTOR ADAPTATION LEARNING FOR BCI-ASSISTED STROKE REHABILITATION

 

                                                       

Ozan Özdenizci
Electronics Engineering, MSc. Thesis, 2016

 

Thesis Jury

Assoc. Prof. Dr. Müjdat Çetin (Thesis Advisor), Assoc. Prof. Dr. Volkan Patoğlu,

Prof. Dr. Zümray Dokur Ölmez

 

 

Date &Time: August 1st, 2016 – 10:40 AM

Place: FENS G032

Keywords: electroencephalogram, motor learning, force-field adaptation,

brain-computer interfaces, stroke rehabilitation

 

Abstract

 

Being one of the most prominent research areas over the last two decades, electroencephalogram (EEG) based brain-computer interface (BCI) technology aims to provide direct brain communication for locked-in patients with severe neuromuscular disabilities and support motor restoration in stroke with recently developing approaches. In the context of EEG-based BCI-assisted stroke rehabilitation, we hypothesize that the extent of brain activities considered in state-of-the-art protocols, which are restricted to haptic feedback of neural activity in primary sensorimotor areas, might be a confounding factor for further progress in this field due to empirical evidence on a variety of brain rhythms being related to the extent of motor deficits. As post-stroke recovery is a form of motor learning, we propose to identify neural correlates of motor learning beyond sensorimotor areas to extend the current focus of BCI-assisted stroke rehabilitation. For this purpose, we designed and implemented a physical force-field adaptation learning experiment under simultaneous EEG recordings with healthy individuals, in which post-stroke recovery processes of patients will be likened to a plausible form of motor learning as such motor adaptation tasks are known to induce internal model formations for motor capabilities within the brain. With the experimental data, we aimed to identify neural correlates of motor adaptation learning during resting-state and pre-movement phases prior to motor execution. We implemented a signal processing and machine learning approach to investigate the relation between kinematic learning performance and neural data. Our results on both resting-state and pre-movement EEG data verify that a broad network of brain regions including and beyond sensorimotor areas are involved in motor adaptation learning with spectral relevance of beta oscillations (15-30 Hz) in particular. We further investigated changes in learning-correlated activities during the course of motor adaptation and discussed how our conclusions come into line with previous empirical evidence of neuroimaging studies to understand human motor behavior. Finally, we propose to exploit these results in a novel BCI-assisted robotic rehabilitation setting.