Abstract
Event Related Potentials (ERPs) are generated in ongoing brain electrical activity due to visual, auditory, or sensory stimuli. These signals have very low SNR and are contaminated by background EEG. Extraction of single trial ERPs from background EEG is a challenging task due to overlapping nature of the frequency bands of ERP and EEG signals and much higher power of EEG than ERPs. In this paper we proposed a method based on wavelet transform and adaptive noise cancelers in order to extract single trial ERPs from background EEG in very low SNR conditions. Simulation results show the superiority of the proposed algorithm over the existing methods. In addition, performance of the algorithm is justified under different noise models namely White Gaussian Noise, Auto Regressive, and Real EEG signals.
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.
