RESUMEN
INTRODUCTION: This research evaluated the feasibility of a hybrid SSVEP + P300 brain computer interface (BCI) for controlling the movement of an avatar in a virtual reality (VR) gaming environment (VR + BCI). Existing VR + BCI gaming environments have limitations, such as visual fatigue, a lower communication rate, minimum accuracy, and poor system comfort. Hence, there is a need for an optimized hybrid BCI system that can simultaneously evoke the strongest P300 and SSVEP potentials in the cortex. METHODS: A BCI headset was coupled with a VR headset to generate a VR + BCI environment. The author developed a VR game in which the avatar's movement is controlled using the user's cortical responses with the help of a BCI headset. Specifically designed visual stimuli were used in the proposed system to elicit the strongest possible responses from the user's brain. The proposed system also includes an auditory feedback mechanism to facilitate precise avatar movement. RESULTS AND CONCLUSIONS: Conventional P300 BCI and SSVEP BCI were also used to control the movements of the avatar, and their performance metrics were compared to those of the proposed system. The results demonstrated that the hybrid SSVEP + P300 BCI system was superior to the other systems for controlling avatar movement.
Asunto(s)
Avatar , Interfaces Cerebro-Computador , Electroencefalografía , Potenciales Relacionados con Evento P300 , Juegos de Video , Adulto , Femenino , Humanos , Masculino , Adulto Joven , Potenciales Relacionados con Evento P300/fisiología , Movimiento/fisiología , Desempeño Psicomotor/fisiologíaRESUMEN
INTRODUCTION: This study proposes a hybrid brain-computer interface (BCI) system that simultaneously evokes steady-state visual evoked potentials (SSVEP) and event-related potentials (P300). The goal of this study was to improve the performance of the current hybrid SSVEP + P300 BCI systems by incorporating inverted faces into visual stimuli. METHODS: In this study, upright and inverted faces were added to visual stimulus to elicit stronger cortical responses in a hybrid SSVEP + P300 BCI. We also considered triggering the P300 signals with facial stimuli and the SSVEP signals with non-facial stimuli. We have tested four paradigms: the upright face paradigm (UF), the inverted face paradigm (IF), the upright face and flicker paradigm (UFF), and the inverted face and flicker paradigm (IFF). RESULTS AND CONCLUSIONS: The results showed that the IFF paradigm evoked more robust cortical responses, which led to enhanced system accuracy and ITR. The IFF paradigm had an average accuracy of 96.6% and a system communication rate of 26.45 bits per second. The UFF paradigm is the best candidate for BCI applications among other paradigms because it provides maximum comfort while maintaining a reasonable ITR.
Asunto(s)
Interfaces Cerebro-Computador , Electroencefalografía , Potenciales Relacionados con Evento P300 , Potenciales Evocados Visuales , Estimulación Luminosa , Humanos , Potenciales Evocados Visuales/fisiología , Potenciales Relacionados con Evento P300/fisiología , Masculino , Femenino , Estimulación Luminosa/métodos , Electroencefalografía/métodos , Adulto Joven , Adulto , Cara , Reconocimiento Visual de Modelos/fisiología , Encéfalo/fisiologíaRESUMEN
BACKGROUND: This study illustrates a hybrid brain-computer interface (BCI) in which steady-state visual evoked potentials (SSVEP) and event-related potentials (P300) are evoked simultaneously. The goal of this study was to improve the performance of the current hybrid SSVEP+P300 BCI systems by incorporating a happy face into visual stimuli. NEW METHOD: In this study, happy and sad faces were added to a visual stimulus to induce stronger cortical signals in a hybrid SSVEP+P300 BCI. Additionally, we developed a paradigm in which SSVEP responses were triggered by non-face stimuli, whereas P300 responses were triggered by face stimuli. We tested four paradigms: happy face paradigm (HF), sad face paradigm (SF), happy face and flicker paradigm (HFF), and sad face and flicker paradigm (SFF). RESULTS AND CONCLUSIONS: The results demonstrated that the HFF paradigm elicited more robust cortical responses, which resulted in enhanced system accuracy and information transfer rate (ITR). The HFF paradigm has a system communication rate of 25.9 bits per second and an average accuracy of 96.1%. Compared with other paradigms, the HFF paradigm is the best choice for BCI applications because it has the highest ITR and maximum level of comfort.