Stroke Neurorehabilitation Augmented by Virtual Reality and EEG-neurofeedback: Neuroimaging-based Validation and Optimization

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Efficacy and brain imaging correlates of an immersive motor imagery BCI-driven VR system for upper limb motor rehabilitation: a clinical case report

Publication . Vourvopoulos, Athanasios; Jorge, Carolina; Abreu, Rodolfo; Figueiredo, Patrícia; Fernandes, Jean-Claude; Bermúdez i Badia, Sergi

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.

2019Journal article

Open access

Finding the optimal time window for increased classification accuracy during motor imagery

Publication . Blanco-Mora, D. A.; Aldridge, A.; Jorge, C.; Vourvopoulos, A.; Figueiredo, P.; Bermúdez i Badia, S.

Motor imagery classification using electroencephalography is based on feature extraction over a length of time, and different configurations of settings can alter the performance of a classifier. Nevertheless, there is a lack of standardized settings for motor imagery classification. This work analyzes the effect of age on motor imagery training performance for two common spatial pattern-based classifier pipelines and various configurations of timing parameters, such as epochs, windows, and offsets. Results showed significant (p ≤ 0.01) inverse correlations between performance and feature quantity, as well as between performance and epoch/window ratio.

2021Conference object

Open access

A study on EEG power and connectivity in a virtual reality bimanual rehabilitation training system

Publication . Blanco-Mora, D. A.; Almeida, Y.; Vieira, C.; Bermúdez i Badia, S.

The study of neural processes that describe bimanual activity in areas such as neurology and rehabilitation are of high interest, in particular for rehabilitation after brain injury. However, brain processes during bimanual motor rehabilitation are not fully understood during stroke rehabilitation. Hence, it is not clear how to exploit them and their possible advantages in an EEG driven Virtual Reality (VR) training. In this work, VR and EEG were combined to study the neural processes in motor areas during bimanual activity in a serious game, involving two kind of movements: Left to Right (L2R) movement (Right handle forward and Left handle backward movements) and Right to Left (R2L) movement (Right handle backward and Left handle forward movements). 10 right handed healthy people (7 Males, 3 Females, 29.9 ± 6.21 years old) participated in this study. As it was expected, differences between rest and bimanual activity conditions (L2R and R2L) were found, surprisingly, on lowest frequency bands, Delta and Theta. More relevant results were found on Delta band at the right Hemisphere and inter-hemispherical relations, specifically for intra-hemispherical connectivity for CPSD relations with p=0.005 (L2R) and p=0.02 (R2L), and power quantified with PSD with p=0.023 (L2R) and p=0.03 (R2L), while inter-hemispherical connectivity got lower values on resting compared to L2R movement with a p=0.015. Besides, comparisons between resting and movement in Theta band showed significant results for inter-hemispherical connectivity (p=0.03, L2R vs Rest, and R2L vs Rest) and differences in power for Left Hemisphere (p=0.05). Finally, non-significant differences were found in motor cortex between the two kind of bimanual activities tested on this work. These results create an opening scenario to test for mirror effect of bimanual activities from one hemisphere to another on populations with hemi paretic conditions, aiming to apply it in a near future as therapy for Stroke Survivors.

2019Conference object

Open access

Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

Publication . Blanco-Mora, D. A.; Aldridge, A.; Jorge, C.; Vourvopoulos, A.; Figueiredo, P.; Bermúdez i Badia, S.

There are many factors outlined in the signal processing pipeline that impact brain–computer interface (BCI) performance, but some methodological factors do not depend on signal processing. Nevertheless, there is a lack of research assessing the effect of such factors. Here, we investigate the impact of VR, immersiveness, age, and spatial resolution on the classifier performance of a Motor Imagery (MI) electroencephalography (EEG)-based BCI in naïve participants. We found significantly better performance for VR compared to non-VR (15 electrodes: VR 77.48 ± 6.09%, non-VR 73.5 ± 5.89%, p = 0.0096; 12 electrodes: VR 73.26 ± 5.2%, non-VR 70.87 ± 4.96%, p = 0.0129; 7 electrodes: VR 66.74 ± 5.92%, non-VR 63.09 ± 8.16%, p = 0.0362) and better performance for higher electrode quantity, but no significant differences were found between immersive and non immersive VR. Finally, there was not a statistically significant correlation found between age and classifier performance, but there was a direct relation found between spatial resolution (electrode quantity) and classifier performance (r = 1, p = 0.0129, VR; r = 0.99, p = 0.0859, non-VR).

2022Journal article

Open access

Brain–computer interfacing with interactive systems-Case study 2

Publication . Vourvopoulos, A.; Niforatos, E.; Bermúdez i Badia, S.; Liarokapis, F.

2021Book part

Open access