Abstract
Electroencephalogram/Event-related-potentials (EEG/ERP) signals have lower amplitude, are popularly known for their nonstationary nature and are easily exposed to other surrounding and biophysical artefacts. In the present work, the first attempt has been made to enhance the EEG/ERP signals using metaheuristically optimised Kalman filter parameters. The adaptive Kalman filter (AKF) parameters on proper tuning provide stable filtering operation. This parameter tuning is achieved metaheuristically by the recently proposed social mimic optimisation algorithm (SMOA). Further, it is tested in various noisy environments and compared with benchmark optimisation algorithms such as particle swarm optimisation algorithm (PSOA) and symbiotic organism search algorithms (SOSA). The proposed methodology manifests better results and surpasses other techniques in the literature. Hence, the proposed method can be used to enhance the EEG/ERP signal.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
