title: Use of a Green Familiar Faces Paradigm Improves P300-Speller Brain-Computer Interface Performance.
abstract: BACKGROUND
  A recent study showed improved performance of the P300-speller when the flashing row or column was overlaid with translucent pictures of familiar faces (FF spelling paradigm). However, the performance of the P300-speller is not yet satisfactory due to its low classification accuracy and information transfer rate.
  
  
  OBJECTIVE
  To investigate whether P300-speller performance is further improved when the chromatic property and the FF spelling paradigm are combined.
  
  
  METHODS
  We proposed a new spelling paradigm in which the flashing row or column is overlaid with translucent green pictures of familiar faces (GFF spelling paradigm). We analyzed the ERP waveforms elicited by the FF and proposed GFF spelling paradigms and compared P300-speller performance between the two paradigms.
  
  
  RESULTS
  Significant differences in the amplitudes of four ERP components (N170, VPP, P300, and P600f) were observed between both spelling paradigms. Compared to the FF spelling paradigm, the GFF spelling paradigm elicited ERP waveforms of higher amplitudes and resulted in improved P300-speller performance.
  
  
  CONCLUSIONS
  Combining the chromatic property (green color) and the FF spelling paradigm led to better classification accuracy and an increased information transfer rate. These findings demonstrate a promising new approach for improving the performance of the P300-speller.
authors.affiliation: School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Li
authors.affiliation: School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China; Hequ Hydrologic Station, Middle Reaches Hydrology and Water Resources Bureau of Yellow River Conservancy Commission, Jinzhong, China.
authors.forename: Shuai
authors.initials: S
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Li
authors.affiliation: School of Engineering, Virginia Commonwealth University, Richmond, United States of America.
authors.forename: Ou
authors.initials: O
authors.lastname: Bai
mesh_terms: D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D003116:Color; D018913:Event-Related Potentials, P300; D005145:Face; D005260:Female; D006801:Humans; D008297:Male; D010775:Photic Stimulation; D021641:Recognition, Psychology; D014584:User-Computer Interface; D014956:Writing; D055815:Young Adult