An implementation of SSVEP-BCI system based on a cluster measure for feature selection

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.