Brain-computer interface based on high frequency steady-state visual evoked potentials: A feasibility study

Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) are systems in which virtual or physical objects are tagged with flicker of different frequencies. When a user focuses on one of the objects its flicker frequency becomes visible in the electroencephalogram (EEG) and so the object on which the user focuses can be determined from brain activity alone. A significant problem inherent to such systems is that typically flicker with frequencies in the range 5 - 30 Hz is used. Flicker in this frequency range is known to elicit easily detectable SSVEPs but is very tiring and annoying for users and can possibly trigger epileptic seizures. In this paper we study the feasibility of using higher frequencies for which the perceived flicker is less intensive. We compare the classification accuracy that can be achieved for stimuli flickering with low frequencies (15 - 20 Hz), medium frequencies (30 - 45 Hz), and high frequencies (50 - 85 Hz). The classification of the data is done with a Bayesian algorithm that learns classification rules and selects optimal electrode pairs. The results show that the medium frequency range can be used to build a high-performance BCI for which the flicker is hardly visible. We also found that for some subjects even high frequency flicker evokes reliably detectable SSVEPs.