  Brain-computer interfaces (BCI) have the potential to play a vital role in
future healthcare technologies by providing an alternative way of communication
and control. More specifically, steady-state visual evoked potential (SSVEP)
based BCIs have the advantage of higher accuracy and higher information
transfer rate (ITR). In order to fully exploit the capabilities of such
devices, it is necessary to understand the features of SSVEP and design the
system considering its biological characteristics. This paper introduces
bio-inspired filter banks (BIFB) for a novel SSVEP frequency detection method.
It is known that SSVEP response to a flickering visual stimulus is frequency
selective and gets weaker as the frequency of the stimuli increases. In the
proposed approach, the gain and bandwidth of the filters are designed and tuned
based on these characteristics while also incorporating harmonic SSVEP
responses. This method not only improves the accuracy but also increases the
available number of commands by allowing the use of stimuli frequencies elicit
weak SSVEP responses. The BIFB method achieved reliable performance when tested
on datasets available online and compared with two well-known SSVEP frequency
detection methods, power spectral density analysis (PSDA) and canonical
correlation analysis (CCA). The results show the potential of bio-inspired
design which will be extended to include further SSVEP characteristic (e.g.
time-domain waveform) for future SSVEP based BCIs.
