@inproceedings{scholars538,
           title = {Estimation of visual evoked potentials for measurement of optical pathway conduction},
         address = {Glasgow},
         journal = {European Signal Processing Conference},
           pages = {2322--2326},
            note = {cited By 2; Conference of 17th European Signal Processing Conference, EUSIPCO 2009 ; Conference Date: 24 August 2009 Through 28 August 2009; Conference Code:91099},
            year = {2009},
            issn = {22195491},
          author = {Yusoff, M. Z. and Kamel, N.},
        keywords = {Brain activity; Clinical environments; Colored noise; Eigenvalue decomposition; Karhunen Loeve Transform (KLT); Noise subspace; Optical pathways; Patient data; Residual noise; Signal sub-space; Sub-space methods; Time domain; Visual evoked potential, Algorithms; Bioelectric potentials; Eigenvalues and eigenfunctions; Electroencephalography; Electrophysiology; Hospital data processing; Principal component analysis; Signal processing; Time domain analysis, Estimation},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863739060&partnerID=40&md5=f4700a392bec33681bd9803133eefd17},
        abstract = {A time domain constrained subspace-based estimator for extracting a visual evoked potential (VEP) from a highly noisy brain activity is proposed. Generally, the desired VEP is corrupted by background electroencephalogram (EEG) behaving as colored noise, making the overall signal-to-noise ratio as low as -10 dB. The estimator is designed to minimize signal distortion, while keeping residual noise below a specified threshold. Also, the algorithm applies a Karhunen-Loeve transform to decorrelate the corrupted VEP signal and decompose it into two parts called signal and noise subspace. Before an inverse Karhunen-Loeve transform is applied, the noise only subspace is discarded. VEP enhancement is therefore achieved by estimating the desired VEP only from the signal subspace. The performance of the filter to detect the latencies of P100's is comprehensively assessed using realistically simulated VEP and EEG data. Later, the effectiveness and validity of the algorithm are evaluated using real patient data recorded in a clinical environment. The results from both experiments show that the estimator generates reasonably low errors and high success rate. {\^A}{\copyright} EURASIP, 2009.}
}