Search results for "Local linear"

showing 4 items of 4 documents

k-Nearest neighbour local linear prediction of scalp EEG activity during intermittent photic stimulation

2011

The characterization of the EEG response to photic stimulation (PS) is an important issue with significant clinical relevance. This study aims to quantify and map the complexity of the EEG during PS, where complexity is measured as the degree of unpredictability resulting from local linear prediction. EEG activity was recorded with eyes closed (EC) and eyes open (EO) during resting and PS at 5, 10, and 15. Hz in a group of 30 healthy subjects and in a case-report of a patient suffering from cerebral ischemia. The mean squared prediction error (MSPE) resulting from k-nearest neighbour local linear prediction was calculated in each condition as an index of EEG unpredictability. The linear or …

AdultMaleComputer sciencePhotic StimulationBiomedical EngineeringBiophysicsElectroencephalographyEyeMachine learningcomputer.software_genreBrain IschemiaYoung AdultIschemiamedicineHumansEEGPredictabilityIntermittent photic stimulationK nearest neighbourPredictability mapAgedScalpLocal linearmedicine.diagnostic_testbusiness.industrySpectrum AnalysisLocal linear predictionElectroencephalographySignal Processing Computer-AssistedPattern recognitionScalp eegmedicine.anatomical_structureScalpSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaCortexLinear ModelsFemaleArtificial intelligencebusinesscomputerPhotic StimulationMedical Engineering & Physics
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Quantifying changes in EEG complexity induced by photic stimulation.

2009

Summary Objectives: This study aims to characterize EEG complexity, measured as the prediction error resulting from nonlinear prediction, in healthy humans during photic stimulation. Methods: EEGs were recorded from 15 subjects with eyes closed (EC) and eyes open (EO), during the baseline condition and during stroboscopic photic stimulation (PS) at 5, 10, and 15 Hz. The mean squared prediction error (MSPE) resulting from nearest neighbor local linear prediction was taken as complexity index. Complexity maps were generated interpolating the MSPE index over a schematic scalp representation. Results: Statistical analysis revealed that: i) EEG shows good predictability in all conditions and see…

AdultMalePhotic StimulationComputer scienceHealth InformaticsElectroencephalographyMachine learningcomputer.software_genreBrain mappingComplexity indexHealth Information ManagementReference ValuesmedicineHumansEEGPredictabilityPredictability mapVisual stimulationHealth InformaticAdvanced and Specialized NursingBrain Mappingmedicine.diagnostic_testbusiness.industryStochastic processLocal linear predictionPattern recognitionElectroencephalographySignal Processing Computer-AssistedNeurophysiologymedicine.anatomical_structureNonlinear DynamicsScalpSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaFemaleArtificial intelligencebusinesscomputerAlgorithmsPhotic StimulationMethods of information in medicine
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Hajłasz–Sobolev imbedding and extension

2011

Abstract The author establishes some geometric criteria for a Hajlasz–Sobolev M ˙ ball s , p -extension (resp. M ˙ ball s , p -imbedding) domain of R n with n ⩾ 2 , s ∈ ( 0 , 1 ] and p ∈ [ n / s , ∞ ] (resp. p ∈ ( n / s , ∞ ] ). In particular, the author proves that a bounded finitely connected planar domain Ω is a weak α -cigar domain with α ∈ ( 0 , 1 ) if and only if F ˙ p , ∞ s ( R 2 ) | Ω = M ˙ ball s , p ( Ω ) for some/all s ∈ [ α , 1 ) and p = ( 2 − α ) / ( s − α ) , where F ˙ p , ∞ s ( R 2 ) | Ω denotes the restriction of the Triebel–Lizorkin space F ˙ p , ∞ s ( R 2 ) on Ω .

Hajłasz–Sobolev extensionHajłasz–Sobolev imbeddingApplied Mathematics010102 general mathematicsTriebel–Lizorkin spaceTriebel–Lizorkin space01 natural sciencesSobolev spaceCombinatoricsHajłasz–Sobolev spaceUniform domainBounded function0103 physical sciencesWeak cigar domain010307 mathematical physicsBall (mathematics)Local linear connectivity0101 mathematicsAnalysisMathematicsJournal of Mathematical Analysis and Applications
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New methods for analysing colour texture based on the Karhunen–Loeve transform and quantification

2004

In this article, we offer an original study on the analysis of the texture of colour images based on Local Linear Transforms (LLT). Our colour approach is based on the separability of the data which reduces the number of texture parameters. We also propose the extension of Run Lengths (RL) and Co-occurrence Matrixes (CM) to colour images. In this respect, two different ways were explored (data merging and quantification). We finally present a comparative study showing the efficiency of the first method (LLT) as well as the complementary nature of the other methods (RL, CM).

Karhunen–Loève theoremLocal linearbusiness.industryExtension (predicate logic)Texture (geology)Co-occurrence matrixArtificial IntelligenceSignal ProcessingComputer visionComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwareMathematicsPattern Recognition
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