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Effect of head-mounted virtual reality and vibrotactile feedback in ERD during motor imagery Brain–computer interface training

Diogo Batista, Gustavo Caetano, Mathis Fleury, Patrícia Figueiredo, Athanasios Vourvopoulos

2023Brain-Computer Interfaces14 citationsDOIOpen Access PDF

Abstract

Brain–computer interfaces (BCIs) can provide a non-muscular channel of control to stroke patients for motor rehabilitation. This can be achieved through the use of motor imagery (MI) training, involving the modulation of sensorimotor rhythms. The practice of MI has been shown to be able to strengthen key motor pathways when reinforced with rewarding feedback. Recently, there has been a growing evidence of the positive impact of embodied virtual reality (VR) and vibrotactile feedback in MI training. Nonetheless, it is not yet clear what the optimal MI-BCI setup is for evoking stronger sensorimotor rhythms in VR. In this study, we investigate the impact of head-mounted VR, and vibrotactile feedback during MI-BCI training in the induced sensorimotor rhythms. To achieve this, 19 healthy subjects performed MI training with embodied VR between four conditions: head-mounted vs. screen VR, with and without vibrotactile feedback; and two control conditions: abstract MI without embodied feedback, and motor execution. The event-related desynchronization (ERD) and the lateralization indices (LI) of the Alpha and Beta EEG rhythms were analyzed in a within-subject design. Results show that the combination of vibrotactile feedback and embodied VR can induce stronger and more lateralized Alpha ERD; nonetheless, LI was not significantly different across conditions.

Topics & Concepts

Brain–computer interfaceMotor imageryEmbodied cognitionVirtual realityElectroencephalographyRehabilitationPhysical medicine and rehabilitationRhythmSensorimotor rhythmLateralization of brain functionPsychologyBrain activity and meditationInterface (matter)Computer scienceNeurofeedbackHuman–computer interactionCognitive psychologyNeuroscienceMedicineArtificial intelligenceParallel computingBubbleInternal medicineMaximum bubble pressure methodEEG and Brain-Computer InterfacesMuscle activation and electromyography studiesNeuroscience and Neural Engineering