title: Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components.
abstract: Brain-computer interfaces (BCIs) can be realized on the basis of steady-state evoked potentials (SSEPs). These types of brain signals resulting from repetitive stimulation have the same fundamental frequency as the stimulation but also include higher harmonics. This study investigated how the classification accuracy of a 4-class BCI system can be improved by incorporating visually evoked harmonic oscillations. The current study revealed that the use of three SSVEP harmonics yielded a significantly higher classification accuracy than was the case for one or two harmonics. During feedback experiments, the five subjects investigated reached a classification accuracy between 42.5% and 94.4%.
authors.affiliation: Laboratory of Brain-Computer Interfaces, Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16a/II, 8010 Graz, Austria. gernot.mueller@tugraz.at
authors.forename: Gernot R
authors.initials: GR
authors.lastname: Müller-Putz
authors.forename: Reinhold
authors.initials: R
authors.lastname: Scherer
authors.forename: Christian
authors.initials: C
authors.lastname: Brauneis
authors.forename: Gert
authors.initials: G
authors.lastname: Pfurtscheller
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003143:Communication Devices for People with Disabilities; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D005583:Fourier Analysis; D006706:Homeostasis / Q000502:physiology; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D010775:Photic Stimulation / Q000379:methods; D014584:User-Computer Interface; D014793:Visual Cortex / Q000502:physiology*