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- NeuRow: an immersive VR environment for motor-imagery training with the use of brain-computer interfaces and vibrotactile feedbackPublication . Bermúdez i Badia, Sergi; Ferreira, André; Vourvopoulos, AthanasiosMotor-Imagery offers a solid foundation for the development of Brain-Computer Interfaces (BCIs), capable of direct brain-to-computer communication but also effective in alleviating neurological impairments. The fusion of BCIs with Virtual Reality (VR) allowed the enhancement of the field of virtual rehabilitation by including patients with low-level of motor control with limited access to treatment. BCI-VR technology has pushed research towards finding new solutions for better and reliable BCI control. Based on our previous work, we have developed NeuRow, a novel multiplatform prototype that makes use of multimodal feedback in an immersive VR environment delivered through a state-of-the-art Head Mounted Display (HMD). In this article we present the system design and development, including important features for creating a closed neurofeedback loop in an implicit manner, and preliminary data on user performance and user acceptance of the system.
- 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).
- EEG correlates of video game experience and user profile in motor-imagery-based brain–computer interactionPublication . Vourvopoulos, Athanasios; Bermúdez i Badia, Sergi; Liarokapis, FotisThrough the use of brain–computer interfaces (BCIs), neurogames have become increasingly more advanced by incorporating immersive virtual environments and 3D worlds. However, training both the user and the systemrequireslongandrepetitivetrialsresultinginfatigueand lowperformance.Moreover,manyusersareunabletovoluntarilymodulatetheamplitudeoftheirbrainactivitytocontrol theneurofeedbackloop.Inthisstudy,wearefocusingonthe effect that gaming experience has in brain activity modulation as an attempt to systematically identify the elements that contribute to high BCI control and to be utilized in neurogamedesign.Basedonthecurrentliterature,wearguethat experienced gamers could have better performance in BCI trainingduetoenhancedsensorimotorlearningderivedfrom gaming. To investigate this, two experimental studies were conducted with 20 participants overall, undergoing 3 BCI sessions,resultingin88EEGdatasets.Resultsindicate(a)an effectfrombothdemographicandgamingexperiencedatato theactivitypatternsofEEGrhythms,and(b)increasedgamingexperiencemightnotincreasesignificantlyperformance, but it could provide faster learning for ‘Hardcore’ gamers.
- RehabNet: a distributed architecture for motor and cognitive neuro-rehabilitationPublication . Vourvopoulos, Athanasios; Faria, Ana Lúcia; Cameirão, Mónica S.; Bermúdez i Badia, SergiEvery year millions of people worldwide suffer from stroke, resulting in motor and/or cognitive disability. As a result, patients experience an increased loss of independence, autonomy and low self-esteem. Evolving to a chronic condition, stroke requires of continuous rehabilitation and therapy. Current ICT approaches, with the use of robotics and Virtual Reality, show some benefits over conventional therapy. However, most of the novel approaches are suitable only for a reduced subset of patients. RehabNet proposes an inclusive approach towards an open and distributed architecture for ‘in-home’ neurorehabilitation and monitoring by means of non-invasive ICT. In this paper we present the RehabNet architecture, its design and the implementation of a combined motor-and-cognitive system for post-stroke rehabilitation.
- Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysisPublication . Vourvopoulos, Athanasios; Bermúdez i Badia, SergiThe use of Brain-Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain's capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training.
- Development and assessment of a self-paced BCI-VR paradigm using multimodal stimulation and adaptive performancePublication . Vourvopoulos, Athanasios; Ferreira, André; Bermúdez i Badia, SergiMotor-Imagery based Brain-Computer Interfaces (BCIs) can provide alternative communication pathways to neurologically impaired patients. The combination of BCIs and Virtual Reality (VR) can provide induced illusions of movement to patients with low-level of motor control during motor rehabilitation tasks. Unfortunately, current BCI systems lack reliability and good performance levels in comparison with other types of computer interfaces. To date, there is little evidence on how BCI-based motor training needs to be designed for transferring rehabilitation improvements to real life. Based on our previous work, we showcase the development and assessment of NeuRow, a novel multiplatform immersive VR environment that makes use of multimodal stimulation through vision, sound and vibrotactile feedback and delivered through a VR Head Mounted Display. In addition, we integrated the Adaptive Performance Engine (APE), a statistical approach to optimize user control in a selfpaced BCI-VR paradigm. In this paper, we describe the development and pilot assessment of NeuRow as well as its integration and assessment with APE.
- Optimizing performance of non-expert users in brain-computer interaction by means of an adaptive performance enginePublication . Ferreira, André; Vourvopoulos, Athanasios; Bermúdez i Badia, SergiBrain–Computer Interfaces (BCIs) are become increasingly more available at reduced costs and are being incorporated into immersive virtual environments and video games for serious applications. Most research in BCIs focused on signal processing techniques and has neglected the interaction aspect of BCIs. This has created an imbalance between BCI classification performance and online control quality of the BCI interaction. This results in user fatigue and loss of interest over time. In the health domain, BCIs provide a new way to overcome motor-related disabilities, promoting functional and structural plasticity in the brain. In order to exploit the advantages of BCIs in neurorehabilitation we need to maximize not only the classification performance of such systems but also engagement and the sense of competence of the user. Therefore, we argue that the primary goal should not be for users to be trained to successfully use a BCI system but to adapt the BCI interaction to each user in order to maximize the level of control on their actions, whatever their performance level is. To achieve this, we developed the Adaptive Performance Engine (APE) and tested with data from 20 naïve BCI users. APE can provide user specific performance improvements up to approx. 20% and we compare it with previous methods. Finally, we contribute with an open motor-imagery datasets with 2400 trials from naïve users.
- Using brain-computer interaction and multimodal virtual-reality for augmenting stroke neurorehabilitationPublication . Vourvopoulos, Athanasios; Badia, Sergi Bermudez iEvery year millions of people suffer from stroke resulting to initial paralysis, slow motor recovery and chronic conditions that require continuous reha bilitation and therapy. The increasing socio-economical and psychological impact of stroke makes it necessary to find new approaches to minimize its sequels, as well as novel tools for effective, low cost and personalized reha bilitation. The integration of current ICT approaches and Virtual Reality (VR) training (based on exercise therapies) has shown significant improve ments. Moreover, recent studies have shown that through mental practice and neurofeedback the task performance is improved. To date, detailed in formation on which neurofeedback strategies lead to successful functional recovery is not available while very little is known about how to optimally utilize neurofeedback paradigms in stroke rehabilitation. Based on the cur rent limitations, the target of this project is to investigate and develop a novel upper-limb rehabilitation system with the use of novel ICT technolo gies including Brain-Computer Interfaces (BCI’s), and VR systems. Here, through a set of studies, we illustrate the design of the RehabNet frame work and its focus on integrative motor and cognitive therapy based on VR scenarios. Moreover, we broadened the inclusion criteria for low mobility pa tients, through the development of neurofeedback tools with the utilization of Brain-Computer Interfaces while investigating the effects of a brain-to-VR interaction.