A novel motor imagery hybrid brain computer interface using EEG and functional transcranial Doppler ultrasound
Khalaf A, Sejdic E, Akcakaya M. A novel motor imagery hybrid brain computer interface using EEG and functional transcranial Doppler ultrasound. Journal Of Neuroscience Methods 2018, 313: 44-53. PMID: 30590086, DOI: 10.1016/j.jneumeth.2018.11.017.Peer-Reviewed Original ResearchTowards optimal visual presentation design for hybrid EEG—fTCD brain–computer interfaces
Khalaf A, Sejdic E, Akcakaya M. Towards optimal visual presentation design for hybrid EEG—fTCD brain–computer interfaces. Journal Of Neural Engineering 2018, 15: 056019. PMID: 30021931, DOI: 10.1088/1741-2552/aad46f.Peer-Reviewed Original ResearchConceptsFunctional transcranial Doppler ultrasoundMental rotationTerms of accuracyTransfer rateFTCD signalsHybrid systemDesign approachReal-time BCIVisual presentationFeasible candidateMotor imageryInterface systemHigh accuracyVisual stimulation techniqueElectrical brain activityBrain-computer interface (BCI) systemsHybrid BCIHybrid combinationCognitive tasksPower spectrumSuch promising resultsHybrid brain-computer interface (BCI) systemFixation crossAccuracyDesignReal-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing System
Hassan A, Khalaf A, Sayed K, Li H, Chen Y. Real-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing System. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2018, 00: 2567-2570. PMID: 30440932, DOI: 10.1109/embc.2018.8512868.Peer-Reviewed Original ResearchConceptsNeuromorphic computing systemsComputing systemsCardiac arrhythmia classificationArrhythmia classificationReal-time processingTerms of accuracyDifferent beat typesAverage testing timeTesting timeAverage accuracyBeat typesBasic building blocksDetection techniquesPower consumptionOverall accuracyExperimental resultsClassificationAccuracySystemBuilding blocks