title: Incremental SSVEP analysis for BCI implementation.
abstract: This work presents an incremental analysis of EEG records containing Steady-State Visual Evoked Potential (SSVEP). This analysis consists of two steps: feature extraction, performed using a statistic test, and classification, performed by a decision tree. The result is a system with high classification rate (a test with six volunteers resulted in an average classification rate of 91.2%), high Information Transfer Rate (ITR) (a test with the same six volunteers resulted in an average value of 100.2 bits/min) and processing time, for each incremental analysis, of approximately 120 ms. These are very good features for an efficient Brain-Computer Interface (BCI) implementation.
authors.affiliation: Department of Engineering and Computation, North Center (CEUNES), Federal University of Espírito Santo (UFES), São Mateus, BRAZIL. sandramuller@ceunes.ufes.br
authors.forename: Sandra Mara
authors.initials: SM
authors.lastname: Torres Müller
authors.forename: Teodiano
authors.initials: T
authors.lastname: Freire Bastos-Filho
authors.forename: Mário
authors.initials: M
authors.lastname: Sarcinelli-Filho
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D008328:Man-Machine Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D014584:User-Computer Interface; D014793:Visual Cortex / Q000502:physiology*