A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected in...

Brain-Computer Interfaces (BCI ’s) enable us to infer intentional control signals from brain activity. The Visual Speller is a BCI based on event related potentials (ERP ’s) in the electroencephalogram, such as the P300 (a positive deflection in the EEG about 300 ms after a rarely occuring stimulus). In the classical paradigm one trial (i.e. prediction of one symbol) consists of successive high...

In brain-computer interfaces (BCI), most of the approaches based on event-related potential (ERP) focus detection P300, aiming for single trial classification a speller task. While this is an important objective, existing P300 BCI still require several repetitions to achieve correct accuracy. Signal processing and machine learning advances in mostly revolve around part, leaving character out sc...

Background. Brain computer interface (BCI) systems by extracting knowledge from brain signals provide a connection channel to the outside world for disabled people, without physiological interfaces. Event-related potentials (ERPs) are specific type of electroencephalography and P300 is one most important ERP components. The critical part P300-based BCI classification step. In this research, an ...