2022
A machine‐learning approach for predicting impaired consciousness in absence epilepsy
Springer M, Khalaf A, Vincent P, Ryu JH, Abukhadra Y, Beniczky S, Glauser T, Krestel H, Blumenfeld H. A machine‐learning approach for predicting impaired consciousness in absence epilepsy. Annals Of Clinical And Translational Neurology 2022, 9: 1538-1550. PMID: 36114696, PMCID: PMC9539371, DOI: 10.1002/acn3.51647.Peer-Reviewed Original Research
2020
Induced bioresistance via BNP detection for machine learning-based risk assessment
So S, Khalaf A, Yi X, Herring C, Zhang Y, Simon M, Akcakaya M, Lee S, Yun M. Induced bioresistance via BNP detection for machine learning-based risk assessment. Biosensors And Bioelectronics 2020, 175: 112903. PMID: 33370705, DOI: 10.1016/j.bios.2020.112903.Peer-Reviewed Original ResearchA probabilistic approach for calibration time reduction in hybrid EEG–fTCD brain–computer interfaces
Khalaf A, Akcakaya M. A probabilistic approach for calibration time reduction in hybrid EEG–fTCD brain–computer interfaces. BioMedical Engineering OnLine 2020, 19: 23. PMID: 32299441, PMCID: PMC7164278, DOI: 10.1186/s12938-020-00765-4.Peer-Reviewed Original ResearchConceptsBrain-computer interfaceClass-conditional distributionsBCI usersSmall training datasetMI paradigmSupport vector machineCalibration time reductionHybrid brain-computer interfaceMotor imageryTraining dataBetter generalizationFeature vectorsVector machineTraining datasetUsersPrevious usersCalibration sessionSimilar datasetsBhattacharyya distanceGeneration paradigmDatasetCalibration requirementsEfficient performance