Please use this identifier to cite or link to this item:
|Type:||Artigo de evento|
|Title:||An Implementation Of Ssvep-bci System Based On A Cluster Measure For Feature Selection|
|Abstract:||The main objective of a brain-computer interface (BCI) is to create alternative communication channels between the brain and a machine using information from cerebral responses. Among the possible paradigms to design a BCI system, this work focuses on Steady-State Visually Evoked Potentials (SSVEP). SSVEP are brain responses synchronized with fast repetitive external visual stimuli. The SSVEP-BCI system is able to meet many of the requirements of a strict BCI, but still needs to reduce the influence of noise on the Electroencephalogram (EEG) signal in order to improve its performance. In this paper, a novel SSVEP-BCI system is presented and analyzed in detail. The system is based on three pillars: spectrum estimation, systematic feature selection-for which different heuristics were proposed here-, and linear classification. © 2014 IEEE.|
|Editor:||IEEE Computer Society|
|Citation:||Issnip Biosignals And Biorobotics Conference, Brc. Ieee Computer Society, v. , n. , p. - , 2014.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.