2023
Brain imaging with portable low-field MRI
Kimberly W, Sorby-Adams A, Webb A, Wu E, Beekman R, Bowry R, Schiff S, de Havenon A, Shen F, Sze G, Schaefer P, Iglesias J, Rosen M, Sheth K. Brain imaging with portable low-field MRI. Nature Reviews Bioengineering 2023, 1: 617-630. PMID: 37705717, PMCID: PMC10497072, DOI: 10.1038/s44222-023-00086-w.Peer-Reviewed Original ResearchSparse k-space dataK-space dataImage reconstructionUseful diagnostic imagesReconstruction algorithmLow power requirementsNew applicationsLF-MRIDiagnostic imagesNew approachNew opportunitiesHardwareConventional systemMachineOngoing developmentPower requirementsAlgorithmImagesTechnological innovationFurther innovationNoise cancellationNoise ratioTechnologyRequirementsInformation
2021
Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
Harper JR, Cherukuri V, O’Reilly T, Yu M, Mbabazi-Kabachelor E, Mulando R, Sheth KN, Webb AG, Warf BC, Kulkarni AV, Monga V, Schiff SJ. Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus. NeuroImage Clinical 2021, 32: 102896. PMID: 34911199, PMCID: PMC8646178, DOI: 10.1016/j.nicl.2021.102896.Peer-Reviewed Original ResearchConceptsDeep learning enhancementLow-quality imagesDeep learningQuality imagesDeep learning algorithmsEnhanced imageRole of machineLearning enhancementImage qualityLearning algorithmAcceptable image qualityReconstruction errorImage resolutionImagesAlgorithmCT imagesLearningCT counterpartsNoise ratioTreatment planningPlanningNew standardStructural errorsExperienced pediatric neurosurgeonsMachine
2014
Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images.
Mandell J, Langelaan J, Webb A, Schiff S. Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images. Journal Of Neurosurgery Pediatrics 2014, 15: 113-24. PMID: 25431902, DOI: 10.3171/2014.9.peds12426.Peer-Reviewed Original ResearchConceptsEdge trackerParticle filterGround vehicle navigationBrain image analysisCT imagesMRI data setsImage segmentationSegmentation algorithmAutonomous airVehicle navigationAccurate edgesNovel algorithmManual segmentationSegmentationMR imagesBrain dataVolumetric brain analysisData setsImage analysisSemiautomatic methodImagesModality independenceHistorical dataAlgorithmMRI data
2010
Variable Down-Selection for Brain-Computer Interfaces
Dias N, Kamrunnahar M, Mendes P, Schiff S, Correia J. Variable Down-Selection for Brain-Computer Interfaces. Communications In Computer And Information Science 2010, 52: 158-172. DOI: 10.1007/978-3-642-11721-3_12.Peer-Reviewed Original ResearchBrain-computer interfaceLarge feature spaceBetter classification performanceLarge dimensionality reductionSynthetic datasetsClassification performanceFeature spaceClassification errorDimensionality reductionBCI datasetsImagery tasksElectrode channelsMovement imagery tasksKey problemVariable subsetHigh performanceAnalysis classifierBCI experimentsAlgorithmDatasetPrincipal component analysisTaskTime limitationsClassifier
2008
Model-based Responses and Features in Brain Computer Interfaces
Kamrunnahar M, Dias N, Schiff S, Gluckman B. Model-based Responses and Features in Brain Computer Interfaces. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2008, 2008: 4482-4485. PMID: 19163711, DOI: 10.1109/iembs.2008.4650208.Peer-Reviewed Original ResearchConceptsBrain-computer interfaceMotor imagery tasksComputer interfaceClassification errorDifferent feature selection methodsLinear discriminant analysisFeature selection methodClassification of tasksDevelopment of BCIsImagery tasksFeature extractionHuman scalp electroencephalographySelection methodRight hand movementsTaskFilter algorithmEEG signalsNovel modelPrincipal component analysisControl systems theoryHand movementsScalp EEG correlatesFeaturesInterfaceAlgorithm
1994
Fast wavelet transformation of EEG
Schiff S, Aldroubi A, Unser M, Sato S. Fast wavelet transformation of EEG. Clinical Neurophysiology 1994, 91: 442-455. PMID: 7529683, DOI: 10.1016/0013-4694(94)90165-1.Peer-Reviewed Original ResearchConceptsFeature extractionWavelet transformOrdinary microprocessorsComputational demandsRedundant samplingComputational timeMultiresolution frameworkData window lengthReal timeWavelet transformationContinuous wavelet transformRapid algorithmWaveletsAnalysis of EEGExtractionWindow lengthBackground noiseTransformAlgorithmPolynomial splinesMicroprocessorFourier transform techniqueRecent workCertain advantagesNumerical techniquesStochastic versus deterministic variability in simple neuronal circuits: I. Monosynaptic spinal cord reflexes
Chang T, Schiff S, Sauer T, Gossard J, Burke R. Stochastic versus deterministic variability in simple neuronal circuits: I. Monosynaptic spinal cord reflexes. Biophysical Journal 1994, 67: 671-683. PMID: 7948680, PMCID: PMC1225409, DOI: 10.1016/s0006-3495(94)80526-0.Peer-Reviewed Original ResearchConceptsTheoretical time seriesEvidence of determinismTime seriesDeterministic equationsNonlinear predictionDeterministic variabilityDeterministic structureSimple neuronal circuitDeterministic behaviorSurrogate dataLocal dispersionStatistical controlLocal flowLong time seriesAutocorrelationEquationsGroup Ia muscle afferentsOscillationsAlgorithmReflex variabilityStateDiscriminating deterministic versus stochastic dynamics in neuronal activity
Schiff S, Sauer T, Chang T. Discriminating deterministic versus stochastic dynamics in neuronal activity. Integrative Psychological And Behavioral Science 1994, 29: 246-261. PMID: 7811645, DOI: 10.1007/bf02691329.Peer-Reviewed Original Research