Search results for "Scalp eeg"

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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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Multiscale Granger causality analysis by à trous wavelet transform

2017

Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the…

Computer scienceGeneralization0206 medical engineering02 engineering and technology01 natural sciencesQuantitative Biology - Quantitative MethodsCausality (physics)WaveletGranger causality0103 physical sciencesTime seriesElectrical and Electronic Engineering010306 general physicsInstrumentationbusiness.industryWavelet transformPattern recognitionFilter (signal processing)multiscale analysi020601 biomedical engineeringUndersamplingscalp EEGQuantitative Biology - Neurons and CognitionSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityWavelet transformArtificial intelligencebusiness
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