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Brain-computer interfaces (BCIs) and their associated technologies have thepotential to shape future forms of communication, control, and security.Specifically, the steady-state visual evoked potential (SSVEP) based BCIs havethe advantages of better recognition accuracy, and higher information transferrate (ITR) compared to other BCI modalities. To fully exploit the capabilitiesof such devices, it is necessary to understand the underlying biologicalfeatures of SSVEPs and design the system considering their inherentcharacteristics. This paper introduces bio-inspired filter banks (BIFBs) forimproved SSVEP frequency recognition. SSVEPs are frequency selective,subject-specific, and their power gets weaker as the frequency of the visualstimuli increases. Therefore, the gain and bandwidth of the filters aredesigned and tuned based on these characteristics while also incorporatingharmonic SSVEP responses. The BIFBs are utilized in the feature extractionstage to increase the separability of classes. This method not only improvesthe recognition accuracy but also increases the total number of availablecommands in a BCI system by allowing the use of stimuli frequencies that elicitweak SSVEP responses. The BIFBs are promising particularly in thehigh-frequency band, which causes less visual fatigue. Hence, the proposedapproach might enhance user comfort as well. The BIFB method is tested on twoonline benchmark datasets and outperforms the compared methods. The resultsshow the potential of bio-inspired design, and the findings will be extended byincluding further SSVEP characteristics for future SSVEP based BCIs.

Bio-inspired Filter Banks for Frequency Recognition of SSVEP-based Brain-computer Interfaces