Bio-Inspired Filter Banks for SSVEP-based Brain-Computer Interfaces
This work addresses the need for more effective SSVEP-based BCIs, which are crucial for healthcare technologies like communication aids, though it appears incremental as it builds on known biological characteristics.
The paper tackled the problem of improving frequency detection in SSVEP-based brain-computer interfaces by introducing bio-inspired filter banks, which increased accuracy and allowed more commands by utilizing weak SSVEP responses, achieving reliable performance compared to existing methods like PSDA and CCA.
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.