title: A zero-training algorithm for EEG single-trial classification applied to a face recognition ERP experiment.
abstract: This paper proposes a machine learning based approach to discriminate between EEG single trials of two experimental conditions in a face recognition experiment. The algorithm works using a single-trial EEG database of multiple subjects and thus does not require subject-specific training data. This approach supports the idea that zero-training classification and on-line detection Brain Computer Interface (BCI) systems are areas with a significant amount of potential.
authors.affiliation: Cuban Neuroscience Center, Havana Cuba. agustin@cneuro.edu.cu
authors.forename: Agustin
authors.initials: A
authors.lastname: Lage-Castellanos
authors.forename: Juan I
authors.initials: JI
authors.lastname: Nieto
authors.forename: Ileana
authors.initials: I
authors.lastname: Quiñones
authors.forename: Eduardo
authors.initials: E
authors.lastname: Martinez-Montes
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D005071:Evoked Potentials; D005145:Face; D006801:Humans; D014796:Visual Perception