An investigation into the use of six facially encoded emotions in brain-computer interfacing

Abstract

Brain-computer interfaces (BCIs) based upon event-related potentials (ERPs) can allow fast and accurate communication. The performance of this type of BCI may be improved by the use of images of faces to evoke ERPs. We suggest that using facially encoded emotions may further improve the performance of this BCI. We also investigate how different facially encoded emotions (happiness, sadness, etc.) compare to one another in terms of BCI performance. A group of 10 healthy participants were asked to attempt to control both an offline and an online BCI in which facially encoded emotions were used to evoke ERPs. Facial stimuli were used to encode six basic emotions, surprise, fear, disgust, anger, happiness, and sadness. These stimuli were compared to colored-circle stimuli. The results demonstrate that facially encoded emotions result in significantly higher accuracies than the colored-ball condition (p = .028). This suggests that ERP-based BCI systems may benefit from the use of facially encoded emotions as stimuli. Additionally, there was a significant effect of facially encoded emotion on the amplitude of the N600 ERP, and ERP peak amplitudes differed significantly depending on the emotion. For example, fear produced ERPs with the largest standard deviation.