IEEE J Biomed Health Inform - Single Trial Visual Evoked Potential Extraction using Partial Least Squares-based Approach.

Tópicos

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Resumo

A single trial extraction of a Visual Evoked Potential (VEP) signal based on the Partial Least Squares (PLS) regression method has been proposed in this paper. This paper has focused on the extraction and estimation of the latencies of P100, P200, P300, N75 and N135 in the artificial Electroencephalograph (EEG) signal. The real EEG signal obtained from the hospital was only concentrated on the P100. The performance of the PLS has been evaluated mainly on the basis of latency error rate of the peaks for the artificial EEG signal, and the mean peak detection and standard deviation for the real EEG signal. The simulation results show that the proposed PLS algorithm is capable of reconstructing the EEG signal into its desired shape of the ideal VEP. For P100, the proposed PLS algorithm is able to provide comparable results to the Generalized Eigenvalue Decomposition (GEVD) algorithm which alters (pre-whitens) the EEG input signal using the pre-stimulation EEG signal. It has also shown better performance for later peaks (P200 and P300). The PLS outperformed not only in positive peaks but also in N75. In P100 the PLS was comparable with the GEVD although N135 was better estimated by GEVD. The proposed PLS algorithm is comparable to GEVD given that PLS does not alter the EEG input signal. The PLS algorithm gives the best estimate to multi-trial ensemble averaging. This research offers benefits such as avoiding patient's fatigue during VEP test measurement in hospital, in BCI applications and in EEG-fMRI integration.

Resumo Limpo

singl trial extract visual evok potenti vep signal base partial least squar pls regress method propos paper paper focus extract estim latenc p p p n n artifici electroencephalograph eeg signal real eeg signal obtain hospit concentr p perform pls evalu main basi latenc error rate peak artifici eeg signal mean peak detect standard deviat real eeg signal simul result show propos pls algorithm capabl reconstruct eeg signal desir shape ideal vep p propos pls algorithm abl provid compar result general eigenvalu decomposit gevd algorithm alter prewhiten eeg input signal use prestimul eeg signal also shown better perform later peak p p pls outperform posit peak also n p pls compar gevd although n better estim gevd propos pls algorithm compar gevd given pls alter eeg input signal pls algorithm give best estim multitri ensembl averag research offer benefit avoid patient fatigu vep test measur hospit bci applic eegfmri integr

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