Clinical NeuroscienceA survey of the dummy face and human face stimuli used in BCI paradigm

- Dummy face stimulus was easy to be edited and had no copyright infringement problems when it was applied to BCI system.
- There was no significant difference in the latency and amplitude of ERPs between the dummy face and the human face stimulus.
- Dummy face pattern obtained as high classification accuracy and information transfer rate as the human face pattern, which would be a good choice for optimizing the stimulus of BCI system.

BackgroundIt was proved that the human face stimulus were superior to the flash only stimulus in BCI system. However, human face stimulus may lead to copyright infringement problems and was hard to be edited according to the requirement of the BCI study. Recently, it was reported that facial expression changes could be done by changing a curve in a dummy face which could obtain good performance when it was applied to visual-based P300 BCI systems.New methodIn this paper, four different paradigms were presented, which were called dummy face pattern, human face pattern, inverted dummy face pattern and inverted human face pattern, to evaluate the performance of the dummy faces stimuli compared with the human faces stimuli.Comparison with existing method(s)The key point that determined the value of dummy faces in BCI systems were whether dummy faces stimuli could obtain as good performance as human faces stimuli. Online and offline results of four different paradigms would have been obtained and comparatively analyzed.ResultsOnline and offline results showed that there was no significant difference among dummy faces and human faces in ERPs, classification accuracy and information transfer rate when they were applied in BCI systems.ConclusionsDummy faces stimuli could evoke large ERPs and obtain as high classification accuracy and information transfer rate as the human faces stimuli. Since dummy faces were easy to be edited and had no copyright infringement problems, it would be a good choice for optimizing the stimuli of BCI systems.