A robust Gauss-Newton algorithm for analyzing Steady-State Visual Evoked Potentials

Steady-State Visual Evoked Potential (SSVEP) Brain-Computer Interfaces (BCIs) are becoming more interesting with increases in demand for robust BCI systems with real-time control capability. This type of BCI is based on collecting the brain signals from visual cortex while the users' attention is toward an exogenous stimulus. Stimulus with constant frequency rate above 4 Hz evokes the SSVEPs. This research uses the data collected from 4 healthy subjects. Each subject participated in test sessions with 4 different LEDs, flickering at 10, 11, 12 and 13 Hz. A 10-order adaptive priori-based robust Gauss-Newton algorithm is adjusted to estimate the brain source signals. Finally, decision detection is based on the maximum Signal to Noise Ratio (SNR). Results are promising an effective method, which could be later developed for implementation of online BCI systems.