Abstract
Steady-state visual evoked potentials-based brain-computer interfaces (SSVEP-BCI) has the advantage of high information transfer rate (ITR) and little user training, and it has a high application value in the field of disability assistance and human-computer interaction. Generally SSVEP-BCI requires a personal computer screen (PC) to display several repetitive visual stimuli for inducing the SSVEP response, which reduces its portability and flexibility. Using augmented reality (AR) glasses worn on the head to display the repetitive visual stimuli could solve the above drawbacks, but whether it could achieve the same accuracy as PC screen in the case of reduced brightness and increased interference is unknown. In current study, we firstly designed 4 stimulus layouts and displayed them with Microsoft HoloLens (AR-SSVEP) glasses, comparison analysis showed that the classification accuracies are influenced by the stimulus layout when the stimulus duration is less than 3s. When the stimulus duration exceeds 3s, there is no significant accuracy difference between the 4 layouts. Then we designed a similar experimental paradigm on PC screen (PC-SSVEP) based on the best layout of AR. Classification results showed that AR-SSVEP achieved similar accuracy with PC-SSVEP when the stimulus duration is more than 3s, but when the stimulus duration is less than 2s, the accuracy of AR-SSVEP is lower than PC-SSVEP. Brain topological analysis indicated that the spatial distribution of SSVEP responses is similar, both of which are strongest in the occipital region. Current study indicated that stimulus layout is a key factor when building SSVEP-BCI with AR glasses, especially when the stimulation time is short.
Highlights
In recent years, brain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEP) has attracted a lot of attention due to its high information transfer rate (ITR) and little user training [1]–[3]
INFLUENCE OF STIMULUS LAYOUT ON augmented reality (AR)-SSVEP CLASSIFICATION We calculated the classification accuracy under different time windows, which all started from the onset time of the stimulus, but the epoch length is different
Since this study focuses on comparing the SSVEP response difference between personal computer screen (PC) screen and AR stimuli, we did not investigate the relationships between these stimulation designs and AR-SSVEP recognition performance
Summary
Brain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEP) has attracted a lot of attention due to its high information transfer rate (ITR) and little user training [1]–[3] It could be used in smart home appliances [4], [5], disability assistance [6]–[9], human-computer interaction [10]–[12], games and entertainment [13], [14] and other fields. The repetitive visual stimulus are rendered by personal computer screen (PC) or light-emitting diode (LED) light [17], and the position of the stimulator is often fixed and inconvenient to move It reduces the portability and flexibility of SSVEP-BCI, making users often have to sit or stand still to complete the interactive tasks, which greatly limits the SSVEP-BCI application in the area of human-computer interaction.
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