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RISS 인기검색어
- 검색키워드
- 저자 : Shahbazi Avarvand, F. (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Objective quality assessment of stereoscopic images with vertical disparity using EEG
Avarvand, Forooz Shahbazi,Bosse, Sebastian,Mü,ller, Klaus-Robert,Schä,fer, Ralf,Nolte, Guido,Wiegand, Thomas,Curio, Gabriel,Samek, Wojciech IOP 2017 Journal of neural engineering Vol.14 No.4
<P> <I>Objective</I>. Neurophysiological correlates of vertical disparity in 3D images are studied in an objective approach using EEG technique. These disparities are known to negatively affect the quality of experience and to cause visual discomfort in stereoscopic visualizations. <I>Approach</I>. We have presented four conditions to subjects: one in 2D and three conditions in 3D, one without vertical disparity and two with different vertical disparity levels. Event related potentials (ERPs) are measured for each condition and the differences between ERP components are studied. Analysis is also performed on the induced potentials in the time frequency domain. <I>Main results</I>. Results show that there is a significant increase in the amplitude of P1 components in 3D conditions in comparison to 2D. These results are consistent with previous studies which have shown that P1 amplitude increases due to the depth perception in 3D compared to 2D. However the amplitude is significantly smaller for maximum vertical disparity (3D-3) in comparison to 3D with no vertical disparity. Our results therefore suggest that the vertical disparity in 3D-3 condition decreases the perception of depth compared to other 3D conditions and the amplitude of P1 component can be used as a discriminative feature. <I>Significance</I>. The results show that the P1 component increases in amplitude due to the depth perception in the 3D stimuli compared to the 2D stimulus. On the other hand the vertical disparity in the stereoscopic images is studied here. We suggest that the amplitude of P1 component is modulated with this parameter and decreases due to the decrease in the perception of depth.</P>
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The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis
Treder, M.S.,Porbadnigk, A.K.,Shahbazi Avarvand, F.,Muller, K.R.,Blankertz, B. ACADEMIC PRESS 2016 NeuroImage Vol. No.
We introduce a novel beamforming approach for estimating event-related potential (ERP) source time series based on regularized linear discriminant analysis (LDA). The optimization problems in LDA and linearly-constrained minimum-variance (LCMV) beamformers are formally equivalent. The approaches differ in that, in LCMV beamformers, the spatial patterns are derived from a source model, whereas in an LDA beamformer the spatial patterns are derived directly from the data (i.e., the ERP peak). Using a formal proof and MEG simulations, we show that the LDA beamformer is robust to correlated sources and offers a higher signal-to-noise ratio than the LCMV beamformer and PCA. As an application, we use EEG data from an oddball experiment to show how the LDA beamformer can be harnessed to detect single-trial ERP latencies and estimate connectivity between ERP sources. Concluding, the LDA beamformer optimally reconstructs ERP sources by maximizing the ERP signal-to-noise ratio. Hence, it is a highly suited tool for analyzing ERP source time series, particularly in EEG/MEG studies wherein a source model is not available.
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