title: A review of classification algorithms for EEG-based brain-computer interfaces.
abstract: In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
authors.affiliation: IRISA/INRIA Rennes, Campus universitaire de Beaulieu, Avenue du Général Leclerc, 35042 RENNES Cedex, France. fabien.lotte@irisa.fr
authors.forename: F
authors.initials: F
authors.lastname: Lotte
authors.forename: M
authors.initials: M
authors.lastname: Congedo
authors.forename: A
authors.initials: A
authors.lastname: Lécuyer
authors.forename: F
authors.initials: F
authors.lastname: Lamarche
authors.forename: B
authors.initials: B
authors.lastname: Arnaldi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D014584:User-Computer Interface