EEG Signal Analysis Methods Based on Steady State Visual Evoked Potential Stimuli for the Development of Brain Computer Interfaces: A Review

Recently, brain computer interface (BCI) research has increased because of its application value in neural engineering and neuroscience, BCI Systems can provide online communication between a human or animal brain and external devices without depending on the normal output pathways of peripheral nerves and muscles. BCI applications include communication devices for disabled people, neuroprotheses and games. The most popular BCIs is based on steady state visual evoked potential (SSVEP) that can be recognized through detecting the dominant frequency components in the recorded electroencephalography (EEG) signals.  BCI performance depends on correctly and fast decoding the user intentions and is critical to employ a reliable signal processing methods to detect and extract the components of de EEG signals recording. In this paper,   mathematical tools used to design brain computer interface (BCI) systems based on electroencephalogram (EEG) signals obtain by visual stimulus are reviewed.