title: An optimized ERP brain-computer interface based on facial expression changes.
abstract: OBJECTIVE
  Interferences from spatially adjacent non-target stimuli are known to evoke event-related potentials (ERPs) during non-target flashes and, therefore, lead to false positives. This phenomenon was commonly seen in visual attention-based brain-computer interfaces (BCIs) using conspicuous stimuli and is known to adversely affect the performance of BCI systems. Although users try to focus on the target stimulus, they cannot help but be affected by conspicuous changes of the stimuli (such as flashes or presenting images) which were adjacent to the target stimulus. Furthermore, subjects have reported that conspicuous stimuli made them tired and annoyed. In view of this, the aim of this study was to reduce adjacent interference, annoyance and fatigue using a new stimulus presentation pattern based upon facial expression changes. Our goal was not to design a new pattern which could evoke larger ERPs than the face pattern, but to design a new pattern which could reduce adjacent interference, annoyance and fatigue, and evoke ERPs as good as those observed during the face pattern.
  
  
  METHODS
  Positive facial expressions could be changed to negative facial expressions by minor changes to the original facial image. Although the changes are minor, the contrast is big enough to evoke strong ERPs. In this paper, a facial expression change pattern between positive and negative facial expressions was used to attempt to minimize interference effects. This was compared against two different conditions, a shuffled pattern containing the same shapes and colours as the facial expression change pattern, but without the semantic content associated with a change in expression, and a face versus no face pattern. Comparisons were made in terms of classification accuracy and information transfer rate as well as user supplied subjective measures.
  
  
  RESULTS
  The results showed that interferences from adjacent stimuli, annoyance and the fatigue experienced by the subjects could be reduced significantly (p < 0.05) by using the facial expression change patterns in comparison with the face pattern. The offline results show that the classification accuracy of the facial expression change pattern was significantly better than that of the shuffled pattern (p < 0.05) and the face pattern (p < 0.05).
  
  
  CONCLUSIONS
  The facial expression change pattern presented in this paper reduced interference from adjacent stimuli and decreased the fatigue and annoyance experienced by BCI users significantly (p < 0.05) compared to the face pattern.
authors.affiliation: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, People's Republic of China.
authors.forename: Jing
authors.initials: J
authors.lastname: Jin
authors.forename: Ian
authors.initials: I
authors.lastname: Daly
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
authors.forename: Xingyu
authors.initials: X
authors.lastname: Wang
authors.forename: Andrzej
authors.initials: A
authors.lastname: Cichocki
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005149:Facial Expression; D005260:Female; D006801:Humans; D008297:Male; D005222:Mental Fatigue / Q000503:physiopathology* / Q000517:prevention & control; D008875:Middle Aged; D010364:Pattern Recognition, Visual / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014793:Visual Cortex / Q000502:physiology*; D055815:Young Adult