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- Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCIPublication . 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).
- Efficacy and brain imaging correlates of an immersive motor imagery BCI-driven VR system for upper limb motor rehabilitation: a clinical case reportPublication . Vourvopoulos, Athanasios; Jorge, Carolina; Abreu, Rodolfo; Figueiredo, Patrícia; Fernandes, Jean-Claude; Bermúdez i Badia, SergiTo 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.
- Comparison of visual and auditory modalities for Upper-Alpha EEG-NeurofeedbackPublication . Bucho, Teresa; Caetano, Gina; Vourvopoulos, Athanasios; Accoto, Floriana; Esteves, Ines; Bermúdez i Badia, Sergi; Rosa, Agostinho; Figueiredo, PatriciaElectroencephalography (EEG) neurofeedback (NF) training has been shown to produce long-lasting effects on the improvement of cognitive function as well as the normalization of aberrant brain activity in disease. However, the impact of the sensory modality used as the NF reinforcement signal on training effectiveness has not been systematically investigated. In this work, an EEG-based NF training system was developed targeting the individual upper alpha (UA) band and using either a visual or an auditory reinforcement signal, so as to compare the effects of the two sensory modalities. Sixteen healthy volunteers were randomly assigned to the Visual or Auditory group, where a radius varying sphere or a volume-varying sound, respectively, reflected the relative amplitude of UA measured at EEG electrode Cz. Each participant underwent a total of four NF sessions, of approximately 40 min each, on consecutive days. Both groups showed significant increases in UA at Cz within sessions, and also across sessions. Effects subsequent to NF training were also found beyond the target frequency UA and scalp location Cz, namely in the lower-alpha and theta bands and in posterior brain regions, respectively. Only small differences were found on the EEG between the Visual and Auditory groups, suggesting that auditory reinforcement signals may be as effective as the more commonly used visual signals. The use of auditory NF may potentiate training protocols conducted under mobile conditions, which are now possible due to the increasing availability of wireless EEG systems.
- Clinical effects of immersive multimodal BCI-VR training after bilateral neuromodulation with rTMS on upper limb motor recovery after stroke. a study protocol for a randomized controlled trialPublication . Sánchez Cuesta, Francisco José; Arroyo-Ferrer, Aida; González-Zamorano, Yeray; Vourvopoulos, Athanasios; Bermúdez i Badia, Sergi; Figueiredo, Patricia; Serrano, José Ignacio; Romero, Juan PabloThe motor sequelae after a stroke are frequently persistent and cause a high degree of disability. Cortical ischemic or hemorrhagic strokes affecting the cortico spinal pathways are known to cause a reduction of cortical excitability in the lesioned area not only for the local connectivity impairment but also due to a contralateral hemisphere inhibitory action. Non-invasive brain stimulation using high frequency repetitive magnetic transcranial stimulation (rTMS) over the lesioned hemisphere and contralateral cortical inhibition using low-frequency rTMS have been shown to increase the excitability of the lesioned hemisphere. Mental representation techniques, neurofeedback, and virtual reality have also been shown to increase cortical excitability and complement conventional rehabilitation. Materials and Methods: We aim to carry out a single-blind, randomized, controlled trial aiming to study the efficacy of immersive multimodal Brain–Computer Interfacing-Virtual Reality (BCI-VR) training after bilateral neuromodulation with rTMS on upper limb motor recovery after subacute stroke (>3 months) compared to neuromodulation combined with conventional motor imagery tasks. This study will include 42 subjects in a randomized controlled trial design. The main expected outcomes are changes in the Motricity Index of the Arm (MI), dynamometry of the upper limb, score according to Fugl-Meyer for upper limb (FMA-UE), and changes in the Stroke Impact Scale (SIS). The evaluation will be carried out before the intervention, after each intervention and 15 days after the last session. Conclusions: This trial will show the additive value of VR immersive motor imagery as an adjuvant therapy combined with a known effective neuromodulation approach opening new perspectives for clinical rehabilitation protocols.
- Finding the optimal time window for increased classification accuracy during motor imageryPublication . 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.