Detection of Visual Evoked Potentials using Ramanujan Periodicity Transform for real time brain computer interfaces

Pouria Saidi, George K. Atia, Azadeh Vosoughi

Abstract: Repetitive visual stimuli induce periodic Visual Evoked Potentials (VEPs) in the brain that can be potentially identified in an EEG trace. The ability to distinguish frequencies and patterns due to different stimuli is the basis for brain computer interfaces (BCIs) used for communication and control of neurologically disabled patients. Since such responses are recorded in presence of high levels of noise from background brain processes, the detection task is rather challenging. In this work, we propose a detection approach for VEPs based on Ramanujan Periodicity Transform matrices (RPT), which have shown promise in detecting periodicities in data. Our results show that the RPT-based approach can outperform conventional spectral techniques and the state-of-the-art correlation analysis, and is more compatible with real-time BCIs which have to work with short duration EEG epochs. The proposed approach is fairly robust to unknown natural latencies in brain response.