2024
Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade
Du Y, Niu J, Xing Y, Li B, Calhoun V. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophrenia Bulletin 2024, sbae110. PMID: 38982882, DOI: 10.1093/schbul/sbae110.Peer-Reviewed Original ResearchArtificial intelligenceSemi-supervised learning methodArtificial intelligence techniquesAccurate diagnosis of SZMultimodal fusionAccurate diagnosis of schizophreniaIntelligence techniquesAI methodsLearning methodsDiagnosis of SZMental disordersSelection methodUnsupervised clusteringMagnetic resonance imagingBiomarker extractionDiagnosis of schizophrenia
2023
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*
Ellis C, Sattiraju A, Miller R, Calhoun V. Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*. 2023, 00: 2466-2473. DOI: 10.1109/bibm58861.2023.10385424.Peer-Reviewed Original ResearchDeep learning methodsLearning methodsTransfer learningEEG datasetManually engineered featuresTransfer learning approachDeep learning modelsDeep learning performanceMachine learning methodsClassification datasetsLearned representationsElectroencephalography classifierDeep learningEEG classificationResting-state electroencephalographyDiagnosis of major depressive disorderRaw electroencephalographyLearning approachLearning modelsMajor depressive disorder diagnosisMajor depressive disorderLearning performanceClassifierDatasetEngineering featuresImproving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis*
Ellis C, Miller R, Calhoun V. Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis*. 2023, 00: 2474-2481. DOI: 10.1109/bibm58861.2023.10385647.Peer-Reviewed Original ResearchDeep learning methodsLearning methodsContext of automated sleep stage classificationDeep learning classifierSleep stage classificationAutomated feature extractionMachine learning methodsImprove explainabilityLearned featuresFeature extractionExplainability methodsAwake samplesLearning classifiersRaw electroencephalographyIncrease model performanceLayer filtersExplainabilityModeling activitiesModel performanceFilterStage classificationClassifierFiltering activityElectroencephalographyFrequency filteringAn Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis
Sattiraju A, Ellis C, Miller R, Calhoun V. An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-Based Schizophrenia Diagnosis. 2023, 00: 255-259. DOI: 10.1109/bibe60311.2023.00048.Peer-Reviewed Original ResearchConvolutional neural networkRobust deep learning approachBaseline convolutional neural networkChannel lossDeep learning methodsDeep learning modelsDeep learning approachDecision support roleExplainability approachesClassifier performanceRobust modelNeural networkExplainable modelsLearning methodsLearning approachLearning modelsAutomated diagnosisImplementation environmentEEG dataDiagnosis of SZExplainabilityRaw EEGTest dataRobustnessBiomarkers of SZNovel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers
Ellis C, Sattiraju A, Miller R, Calhoun V. Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193605.Peer-Reviewed Original ResearchEffective Training Strategy for NN Models of Working Memory Classification with Limited Samples
Suresh P, Ray B, Thapaliya B, Farahdel B, Kazemivash B, Chen J, Duan K, Calhoun V, Liu J. Effective Training Strategy for NN Models of Working Memory Classification with Limited Samples. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230722.Peer-Reviewed Original ResearchTraining strategyNeural networkData-hungry techniquesNN modelImage featuresSets of hyperparametersMachine learning methodsMachine learning modelsTrained NN modelModel performanceHigh memory capacityImbalanced samplesLearning methodsMemory capacityBrain imaging featuresSuboptimal solutionLearning modelsNetwork configurationEffective training strategyEfficient reuseWorking memory capacityTask-specificData conditionsBiomedical imagingNetwork