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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.
- 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.