Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

• Autores: Floriano, Alan; Delisle Rodriguez, Denis; Diez, Pablo Federico; Bastos Filho, Teodiano Freire
• Año de publicación: 2020
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: BRAIN-COMPUTER INTERFACE; DEPTH-OF-FIELD; EEG; HAIRLESS AREAS
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Background and Objective: Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears). Methods: Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method. Results: The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min. Conclusion: These findings contribute to the development of more safe and practical BCI.
Fil: Floriano, Alan. Universidade Federal do Espírito Santo; Brasil
Fil: Delisle Rodriguez, Denis. Universidade Federal do Espírito Santo; Brasil
Fil: Diez, Pablo Federico. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina
Fil: Bastos Filho, Teodiano Freire. Universidade Federal do Espírito Santo; Brasil