@inproceedings{scholars146,
         address = {Kuala Lumpur},
           title = {Estimation of visual evoked potentials using a signal subspace approach},
         journal = {2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007},
           pages = {1157--1162},
            note = {cited By 4; Conference of 2007 International Conference on Intelligent and Advanced Systems, ICIAS 2007 ; Conference Date: 25 November 2007 Through 28 November 2007; Conference Code:74506},
             doi = {10.1109/ICIAS.2007.4658566},
            year = {2007},
            isbn = {1424413559; 9781424413553},
          author = {Yusoff, M. Z. and Kamel, N. and Hani, A. F. M.},
        abstract = {Extraction of visual evoked potentials (VEPs) from the human brain is generally very difficult due to its poor signal-to-noise ratio (SNR) property. A signal subspace technique is presented to estimate VEPs hidden inside highly colored electroencephalogram (EEG) noise. This method is borrowed and modified from signal subspace techniques originally used for enhancing speech corrupted by colored noise. The signal subspace is estimated by applying eigenvalue decomposition on the approximated signal covariance matrix. The signal subspace-based algorithm is able to satisfactorily extract the P100, P200 and P300 peak latencies from artificially generated noisy VEPs. The simulation results show that the estimator maintains an average success rate of 87  with an average percentage error of less than 9 , when subjected to SNR from 0 dB to -10 dB. {\^A}{\copyright}2007 IEEE.},
        keywords = {Acoustic intensity; Covariance matrix; Eigenvalues and eigenfunctions; Electroencephalography; Electrophysiology; Heat conduction; Real time systems; Signal to noise ratio, Colored EEG noise; Eigenvalue decomposition; Nerve conduction; Subspace; Visual evoked potentials, Signal analysis},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-57949115111&doi=10.1109\%2fICIAS.2007.4658566&partnerID=40&md5=f21090ba7a2672274c8d9a3f609d41b5}
}