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One and Two Dimensional Convolutional Neural Networks for Seizure Detection Using EEG Signals.

, , and . EUSIPCO, page 1387-1391. IEEE, (2020)

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Blind speech signal separation based on non-stationary and colored characteristics., , , , and . ISSPA, page 491-494. IEEE, (2005)Multi-domain feature analysis for depression: A study of N170 in time, frequency and spatial domains simultaneously., , , , , and . EMBC, page 5986-5989. IEEE, (2013)Double coupled canonical polyadic decomposition of third-order tensors: Algebraic algorithm and relaxed uniqueness conditions., , , and . Signal Process. Image Commun., (2019)Dimension reduction for individual ica to decompose FMRI during real-world experiences: principal component analysis vs. canonical correlation analysis., , , , , , , and . ESANN, (2013)Data model conversion for independent component analysis to extract brain signals., and . iCAST, page 187-192. IEEE, (2011)Multi-domain Feature of Event-Related Potential Extracted by Nonnegative Tensor Factorization: 5 vs 14 Electrodes EEG Data., , , , , , and . LVA/ICA, volume 7191 of Lecture Notes in Computer Science, page 502-510. Springer, (2012)Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm., , and . ICASSP, page 3457-3461. IEEE, (2019)Multi-domain collaborative spectrum sensing and power control in presence of multiple primary users., , , , and . ICCS, page 369-373. IEEE, (2014)Generation of stimulus features for analysis of FMRI during natural auditory experiences., , , , , , and . EUSIPCO, page 2490-2494. IEEE, (2014)Fast and effective model order selection method to determine the number of sources in a linear transformation model., , , , and . EUSIPCO, page 1870-1874. IEEE, (2012)