Aya Khalaf
Postdoctoral Associate
Research & Publications
Biography
Coauthors
Selected Publications
- An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theoryMelloni L, Mudrik L, Pitts M, Bendtz K, Ferrante O, Gorska U, Hirschhorn R, Khalaf A, Kozma C, Lepauvre A, Liu L, Mazumder D, Richter D, Zhou H, Blumenfeld H, Boly M, Chalmers D, Devore S, Fallon F, de Lange F, Jensen O, Kreiman G, Luo H, Panagiotaropoulos T, Dehaene S, Koch C, Tononi G. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE 2023, 18: e0268577. PMID: 36763595, PMCID: PMC9916582, DOI: 10.1371/journal.pone.0268577.
- Human visual consciousness involves large scale cortical and subcortical networks independent of task report and eye movement activityKronemer S, Aksen M, Ding J, Ryu J, Xin Q, Ding Z, Prince J, Kwon H, Khalaf A, Forman S, Jin D, Wang K, Chen K, Hu C, Agarwal A, Saberski E, Wafa S, Morgan O, Wu J, Christison-Lagay K, Hasulak N, Morrell M, Urban A, Todd Constable R, Pitts M, Mark Richardson R, Crowley M, Blumenfeld H. Human visual consciousness involves large scale cortical and subcortical networks independent of task report and eye movement activity. Nature Communications 2022, 13: 7342. PMID: 36446792, PMCID: PMC9707162, DOI: 10.1038/s41467-022-35117-4.
- A machine‐learning approach for predicting impaired consciousness in absence epilepsySpringer 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.
- Early neural activity changes associated with stimulus detection during visual conscious perceptionKhalaf A, Kronemer SI, Christison-Lagay K, Kwon H, Li J, Wu K, Blumenfeld H. Early neural activity changes associated with stimulus detection during visual conscious perception. Cerebral Cortex 2022, 33: 1347-1360. PMID: 35446937, DOI: 10.1093/cercor/bhac140.
- Early cortical signals in visual stimulus detectionKwon H, Kronemer SI, Christison-Lagay KL, Khalaf A, Li J, Ding JZ, Freedman NC, Blumenfeld H. Early cortical signals in visual stimulus detection. NeuroImage 2021, 244: 118608. PMID: 34560270, DOI: 10.1016/j.neuroimage.2021.118608.
- Induced bioresistance via BNP detection for machine learning-based risk assessmentSo 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.
- EEG-based Neglect Detection for Stroke PatientsKocanaogullari D, Mak J, Kersey J, Khalaf A, Ostadabbas S, Wittenberg G, Skidmore E, Akcakaya M. EEG-based Neglect Detection for Stroke Patients. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2020, 00: 264-267. PMID: 33017979, DOI: 10.1109/embc44109.2020.9176378.
- A probabilistic approach for calibration time reduction in hybrid EEG–fTCD brain–computer interfacesKhalaf 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.
- Hybrid EEG–fTCD Brain–Computer InterfacesKhalaf A, Sejdic E, Akcakaya M. Hybrid EEG–fTCD Brain–Computer Interfaces. 2020, 295-314. DOI: 10.1007/978-3-030-34784-0_15.
- Analysis of multimodal physiological signals within and between individuals to predict psychological challenge vs. threatKhalaf A, Nabian M, Fan M, Yin Y, Wormwood J, Siegel E, Quigley K, Barrett L, Akcakaya M, Chou C, Ostadabbas S. Analysis of multimodal physiological signals within and between individuals to predict psychological challenge vs. threat. Expert Systems With Applications 2020, 140: 112890. DOI: 10.1016/j.eswa.2019.112890.
- A Machine Learning Approach for Classifying Faults in Microgrids using Wavelet DecompositionKhalaf A, Hassan H, Emes A, Akcakaya M, Grainger B. A Machine Learning Approach for Classifying Faults in Microgrids using Wavelet Decomposition. 2019, 00: 1-6. DOI: 10.1109/mlsp.2019.8918774.
- Bhattacharyya Distance-based Transfer Learning for a Hybrid Eeg-ftcd Brain-computer InterfaceDagois E, Khalaf A, Sejdic E, Akcakaya M. Bhattacharyya Distance-based Transfer Learning for a Hybrid Eeg-ftcd Brain-computer Interface. 2019, 00: 3082-3086. DOI: 10.1109/icassp.2019.8683308.
- EEG-fTCD hybrid brain–computer interface using template matching and wavelet decompositionKhalaf A, Sejdic E, Akcakaya M. EEG-fTCD hybrid brain–computer interface using template matching and wavelet decomposition. Journal Of Neural Engineering 2019, 16: 036014. PMID: 30818297, DOI: 10.1088/1741-2552/ab0b7f.
- Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interfaceKhalaf A, Sejdic E, Akcakaya M. Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface. Journal Of Neuroscience Methods 2019, 320: 98-106. PMID: 30946880, DOI: 10.1016/j.jneumeth.2019.03.018.
- Mutual Information for Transfer Learning in SSVEP Hybrid EEG-fTCD Brain-Computer InterfacesKhalaf A, Sejdic E, Akcakaya M. Mutual Information for Transfer Learning in SSVEP Hybrid EEG-fTCD Brain-Computer Interfaces. 2019, 00: 941-944. DOI: 10.1109/ner.2019.8717018.
- Transfer Learning for a Multimodal Hybrid EEG-fTCD Brain–Computer InterfaceDagois E, Khalaf A, Sejdic E, Akcakaya M. Transfer Learning for a Multimodal Hybrid EEG-fTCD Brain–Computer Interface. IEEE Sensors Letters 2019, 3: 1-4. DOI: 10.1109/lsens.2018.2879466.
- A novel motor imagery hybrid brain computer interface using EEG and functional transcranial Doppler ultrasoundKhalaf 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.
- Towards optimal visual presentation design for hybrid EEG—fTCD brain–computer interfacesKhalaf 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.
- Real-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing SystemHassan 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.
- EEG-based neglect assessment: A feasibility studyKhalaf A, Kersey J, Eldeeb S, Alankus G, Grattan E, Waterstram L, Skidmore E, Akcakaya M. EEG-based neglect assessment: A feasibility study. Journal Of Neuroscience Methods 2018, 303: 169-177. PMID: 29614297, PMCID: PMC7156006, DOI: 10.1016/j.jneumeth.2018.03.019.
- A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machinesKhalaf A, Sybeldon M, Sejdic E, Akcakaya M. A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines. Journal Of Neuroscience Methods 2017, 293: 174-182. PMID: 29017899, DOI: 10.1016/j.jneumeth.2017.10.003.
- An EEG and fTCD Based BCI for ControlKhalaf A, Sybeldon M, Sejdic E, Akcakaya M. An EEG and fTCD Based BCI for Control. 2016, 1285-1289. DOI: 10.1109/acssc.2016.7869581.
- Convolutional Neural Networks for Deep Feature Learning in Retinal Vessel SegmentationKhalaf A, Yassine I, Fahmy A. Convolutional Neural Networks for Deep Feature Learning in Retinal Vessel Segmentation. 2016, 385-388. DOI: 10.1109/icip.2016.7532384.
- A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machinesKhalaf A, Owis M, Yassine I. A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machines. Expert Systems With Applications 2015, 42: 8361-8368. DOI: 10.1016/j.eswa.2015.06.046.
- Spectral Correlation Analysis for Microcalcification Detection in Digital Mammogram ImagesKhalaf A, Yassine I. Spectral Correlation Analysis for Microcalcification Detection in Digital Mammogram Images. 2015, 88-91. DOI: 10.1109/isbi.2015.7163823.
- Arrhythmia Classification Based on Novel Distance Series Transform of Phase Space TrajectoriesSayed K, Khalaf A, Kadah Y. Arrhythmia Classification Based on Novel Distance Series Transform of Phase Space Trajectories. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2015, 2015: 5195-5198. PMID: 26737462, DOI: 10.1109/embc.2015.7319562.
- Image Features of Spectral Correlation Function for Arrhythmia ClassificationKhalaf A, Owis M, Yassine I. Image Features of Spectral Correlation Function for Arrhythmia Classification. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2015, 2015: 5199-5202. PMID: 26737463, DOI: 10.1109/embc.2015.7319563.
- Novel Features for Microcalcification Detection in Digital Mammogram Images Based on Wavelet and Statistical AnalysisKhalaf A, Yassine I. Novel Features for Microcalcification Detection in Digital Mammogram Images Based on Wavelet and Statistical Analysis. 2015, 1825-1829. DOI: 10.1109/icip.2015.7351116.