Statistical evaluation of a novel SSVEP-BCI stimulation setup based on depth-of-field

Abstract

Introduction:The main drawback of a Brain-computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) that detects the emergence of visual evoked potentials (VEP) in reaction to flickering stimuli is its muscular dependence due to users must redirect their gaze to put the target stimulus in their field of view.In this work, a novel setup is evaluated in which two stimuli are placed together in the center of users' field of view, but with dissimilar distances from them, so that the target selection is performed by focus shifting instead of head, neck and/or eyeball movements. Methods: A model of VEP generation for the novel setup was developed. The Spectral F-test based on Bartett periodogram was used to evaluate the null hypothesis of absence of effects of the non-focused stimulus (NFS) within the VEP elicited by the focused stimulus (FS).To reinforce that there is not statistical evidence to support the presence of NFS effects, the PSDA detection method was employed to find the frequency of FS. Electroencephalographic signals of nine subjects were recorded. Results: Approximately in 80% of the tests, the null hypothesis with 5% level of significance was non-rejected at the fundamental frequency of NFS. The average of the accuracy rate attained with PSDA detection method was 79.4%. Conclusion: Results of this work become further evident to state that if the focused stimulus (FS) will be able to elicit distinguishable VEP pattern regardless the non-focused stimulus (NFS) is also present.

Decoding overt shifts of attention in depth through pupillary and cortical frequency tagging

“…The present study was aimed at extending our previous work on PAR-based assistive communication (Ponzio et al, 2019) (Roatta et al, 2019) by exploring frequency tagging with multiple signals. We combined pupil frequency tagging (Stoll et al, 2013) and accommodation-induced SSVEPs modulation (Cotrina et al, 2015, Cotrina et al, 2017 to show the feasibility of a BCI for binary communication that exploits a simple voluntary response -shifting the gaze from a far to a near target as a proxy for a "Yes" response and holding the gaze on a far target as a proxy for a "No" response. The ultimate goal is to provide a tool to give the chance to those (C)LIS patients who for various reasons are not only unable to control residual eye movements but in whom even the PAR is unreliable (Roatta et al, 2019), of sustaining nonetheless a simple form of communication.…”

“…A previous study also used cortical frequency taggingbut not pupil frequency tagging -to decode shifts of attention in depth with the goal of enabling binary communication (Cotrina et al, 2015, Cotrina et al, 2017. In that study, observers shifted the gaze between the two flickering targets, thus that solution did not achieve an independent BCI.…”

“…The present study was aimed at extending our previous work on PAR-based assistive communication (Ponzio et al 2019 by exploring frequency tagging with multiple signals. We combined pupil frequency tagging (Stoll et al 2013) and accommodation-induced SSVEPs modulation (Cotrina et al 2015(Cotrina et al , 2017 to show the feasibility of a BCI for binary communication that exploits a simple voluntary response-shifting the gaze from a far to a near target as a proxy for a 'Yes' response and holding the gaze on a far target as a proxy for a 'No' response.…”

“…By placing two targets in front of an observer, one near and one far, each with a distinct luminance oscillation frequency, it is expected that focusing attention to one or the other target would result in the pupil oscillating more intensely at one or the other frequency, respectively (pupil frequency tagging). As to the latter (SSVEPs), we relied on cortical frequency tagging associated to gaze shifts in depth (Cotrina et al, 2017) (Cotrina et al, 2015). These authors developed a BCI where the evoked cortical oscillations reflected the luminance oscillation frequency of the far or near target, depending on which target the observer's gaze focused to.…”

Ambientes Virtuais Para Treinamento De Interfaces Humano-Computador Utilizadas Em Tecnologias Assistivas

“…Essa IHC utiliza Potenciais Evocados Visuais de Estado Estacionário (SSVEP) por profundidade de campo [7] (Figura 3). Esse trabalho utilizou esses AVs para avaliar a segurança de utilização do dispositivo SSVEP desenvolvido, além de proporcionar a possibilidade de testar o dispositivo com vários usuários, gerando assim, dados para testes estatísticos importantes para a validação do dispositivo desenvolvido.…”