A Self-Paced and Calibration-Less SSVEP-Based Brain–Computer Interface Speller

Abstract

A brain–computer interface (BCI) is a communication system based on neural activity. Its goal is to provide a new output channel for the brain that requires voluntary control. We propose a new self-paced BCI speller based on the detection of steady-state visual evoked potential (SSVEP). The speller does not require any training from the user or from the signal processing part. The system is ready once the subject is prepared. The speller introduces a selection based on a decision tree and an undo command for correcting eventual errors. It was tested on eight healthy subjects who had no prior experience with the application. The average accuracy and information transfer rate are 92.25% and 37.62 bits per minute, which is translated in the speller with an average speed of 5.51 letters per minute