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نویسندگان
چکیده
Parametric modeling strategies are explored in conjunction with Linear Discriminant Analysis (LDA) to facilitate an Electroencephalogram (EEG) based direct-brain interface. A left/right self-paced typing exercise is analysed by employing an AutoRegressive (AR) model for feature extraction and an AutoRegressive with Exogenous input (ARX) model for combined filtering and feature extraction. Modeling both the signal and noise is found to be more effective than modeling the noise alone with the former yielding a classification accuracy of 81.0% and the latter an accuracy of 57.4%.
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Abstract-Papers up to 4 pages should be submitted using this format
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Christophe Jeannin, Pascal Perrier, Yohan Payan, André Dittmar, Brigitte Grosgogeat Institut de la Communication Parlée, INPG, Grenoble, France 2 TIMC, Grenoble, France 3 Laboratoire des microsystèmes et microcapteurs biomédicaux, INSA, Lyon, France 4 Laboratoire d’Etude des Interfaces et des biofilms en Odontologie, Lyon, France 5 Hospices Civils de Lyon, Service d’odontologie, Lyon, France Ch...
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Abstract This paper presents a robust watermarking for still digital images based on schur factorization and Singular Value Decomposition (SVD). In this paper, image is decomposed into 8 8 blocks and after applying schur factorization, the stable largest eigen values of the upper triangle is used as robust locations for embedding watermark. The matrix array formed by largest eigen values furt...
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The assumption that cerebrospinal fluid (CSF) inflow and outflow is an essential element of an intracranial compartment (IC) model has been recently questioned [3]. The idea proposed in [3] that the free displacement of the CSF is one of the significant sources of the brain compliance is supported in this paper by computing an overall system compliance with a novel algorithm. The compliance obt...
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