Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report

Athanasios Vourvopoulos, Carolina Jorge, Rodolfo Abreu, Patrícia Figueiredo, Jean-Claude Fernandes, Sergi Bermúdez I Badia, Athanasios Vourvopoulos, Carolina Jorge, Rodolfo Abreu, Patrícia Figueiredo, Jean-Claude Fernandes, Sergi Bermúdez I Badia

Abstract

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.

Keywords: EEG; brain-computer interface; fMRI; neurorehabilitation; virtual-reality.

Figures

FIGURE 1
FIGURE 1
Experimental setup, including: (A) the wireless EEG system; (B) the Oculus HMD, together with headphones reproducing the ambient sound from the virtual environment; (C) the vibrotactile modules supported by a custom-made table-tray, similar to the wheelchair trays used for support; (D) the visual feedback with NeuRow game. A written informed consent was obtained for the publication of this image.
FIGURE 2
FIGURE 2
BCI Protocol: (A) Intervention stages including the setup, training, resting period and finally the BCI task. (B) The training stages. (C) Training feedback distributed in 24 epochs per class (left|right)
FIGURE 3
FIGURE 3
fMRI protocol. (a) Motor-Execution feedback, (b) Motor-Imagery feedback with directional arrows, (c) Motor-Observation feedback of NeuRow.
FIGURE 4
FIGURE 4
VMIQ-2 subscales comparison of Pre, Post and Follow-up scores with healthy data
FIGURE 5
FIGURE 5
(i) LDA classification performance over time within 10 sessions, (ii) distribution of performance.
FIGURE 6
FIGURE 6
LDA Comparison with healthy. Statistically significant differences between Case-study, VR and non-VR groups has been observed (∗p < 0.05).
FIGURE 7
FIGURE 7
EEG spectral power comparison Pre-Post the intervention with healthy user data.
FIGURE 8
FIGURE 8
ERS/ERD during Left and Right MI for both Mu and Beta bands.
FIGURE 9
FIGURE 9
ERS/ERD activation maps during left (lesioned) hand motor-imagery. Significant ERD is illustrated with blue.
FIGURE 10
FIGURE 10
Lateralization index for Mu and Beta bands across all sessions.
FIGURE 11
FIGURE 11
fMRI activation maps of the motor imagery condition for the left and right hand, at the pre-intervention, post-intervention and follow-up recording sessions. The Nvox and Zmax values are associated with the hemisphere contralateral to the hand. All maps have a threshold at Z > 2.3, except for the one of the motor-imagery of the right hand at the post-intervention (Z > 4.0), because of the substantial higher Zmax.

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