title: Exploiting the temporal patterning of transient VEP signals: a statistical single-trial methodology with implications to brain-computer interfaces (BCIs).
abstract: BACKGROUND
  When visual evoked potentials (VEPs) are deployed in brain-computer interfaces (BCIs), the emphasis is put on stimulus design. In the case of transient VEPs (TVEPs) brain responses are never treated individually, i.e. on a single-trial (ST) basis, due to their poor signal quality. Therefore their main characteristic, which is the emergence during early latencies, remains unexplored.
  
  
  METHODS
  Following a pattern-analytic methodology, we investigated the possibility of using single-trial TVEP responses to differentiate between the different spatial locations where a particular visual stimulus appeared and decide whether it was attended or unattended by the subject.
  
  
  RESULTS
  Covert spatial attention modulates the temporal patterning of TVEPs in such a way that a brief ST-segment, from a single synthesized sensor, is sufficient for a Mahalanobis-Taguchi (MT) system to decode subject's intention.
  
  
  CONCLUSIONS
  In contrast to previous VEP-based approaches, stimulus-related information and user's intention are being decoded from transient ST-signals via exploiting aspects of brain response in the temporal domain.
  
  
  CONCLUSIONS
  We demonstrated that in the TVEP signals there is sufficient discriminative information, coming in the form of a temporal code. We were able to introduce an efficient scheme that can fully exploit this information for the benefit of online classification. The measured performance brings high expectations for incorporating these ideas in BCI-control.
authors.affiliation: Department of Informatics, Aristotle University of Thessaloniki, Greece.
authors.forename: D
authors.initials: D
authors.lastname: Liparas
authors.affiliation: AIIA Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Greece.
authors.forename: S I
authors.initials: SI
authors.lastname: Dimitriadis
authors.affiliation: AIIA Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Greece. Electronic address: laskaris@aiia.csd.auth.gr.
authors.forename: N A
authors.initials: NA
authors.lastname: Laskaris
authors.affiliation: ICCS, National Technical University of Athens, Greece.
authors.forename: A
authors.initials: A
authors.lastname: Tzelepi
authors.affiliation: AIIA Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Greece.
authors.forename: K
authors.initials: K
authors.lastname: Charalambous
authors.affiliation: Department of Informatics, Aristotle University of Thessaloniki, Greece.
authors.forename: L
authors.initials: L
authors.lastname: Angelis
mesh_terms: D001288:Attention / Q000502:physiology; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D004192:Discrimination, Psychological; D004569:Electroencephalography; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D007839:Functional Laterality; D006801:Humans; D008297:Male; D010775:Photic Stimulation; D014584:User-Computer Interface; D014796:Visual Perception / Q000502:physiology
keywords: BCI; Decoding; Mahalanobis-Taguchi system; Transient VEPs