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AbstractBackground: Some studies have proven that a conventional visual brain computer interface (BCI) based on overt attention cannot be used effectively when eye movement control is not possible. To solve this problem, a novel visual-based BCI system based on covert attention and feature attention had been proposed and was called the gaze-independent BCI. Color and shape difference between stimuli and backgrounds have generally been used in examples of gaze-independent BCIs. Recently, a new paradigm based on facial expression change had been presented, and obtained high performance. However, some facial expressions were so similar that users couldn’t tell them apart. Especially they were presented at the same position in a rapid serial visual presentation (RSVP) paradigm. Consequently, the performance of BCIs is reduced.New Method: In this paper, we combined facial expressions and colors to optimize the stimuli presentation in the gaze-independent BCI. This optimized paradigm was called the colored dummy face pattern. It is suggested that different colors and facial expressions could help subjects to locate the target and evoke larger event-related potentials (ERPs). In order to evaluate the performance of this new paradigm, two other paradigms were presented, called the grey dummy face pattern and the colored ball pattern. Comparison with Existing Method(s): The key point that determined the value of the colored dummy faces stimuli in BCI systems were whether dummy face stimuli could obtain higher performance than grey faces or colored balls stimuli. Ten healthy subjects (7 male, aged 21-26 years, mean 24.5±1.25) participated in our experiment. Online and offline results of four different paradigms were obtained and comparatively analyzed.Results: The results showed that the colored dummy face pattern could evoke higher P300 and N400 ERP amplitudes, compared with the grey dummy face pattern and the colored ball pattern. Online results showed that the colored dummy face pattern had a significant advantage in terms of classification accuracy (p |
