Rahul Upadhyay

About

Dr. Rahul Upadhyay is an Associate Professor at Thapar Institute of Engineering and Technology, Patiala. He is associated with the institute's AI & Biomedical Imaging Research Laboratory. The laboratory aims to develop novel Biomedical imaging datasets and methods to process and classify datasets for realizing Computer Assisted Diagnosis techniques. His research team led by Dr. Upadhyay is engaged in developing software solutions for training subjects for operating synchronous and asynchronous Brain-Computer Interface systems. The team develops efficient Electroencephalogram (EEG) cleaning and classification algorithms for medical applications. Dr. Upadhyay works in the area of Artificial Intelligence, and Biomedical Image and Signal processing to develop assistive technological solutions. He completed his Postdoctoral Research with the Reilly Lab, Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland. He has published many papers in journals and conferences of international repute. He has chaired and organized various international conferences and workshops and delivered keynote speeches.

Employment

• Thapar Institute of Engineering & Technology Associate Professor

Education

• Trinity College Dublin Post-Doc

Projects & Funding

Projects & funding information is unavailable.

Publications (7)

• How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review
  IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023 23 citations DOI: 10.1109/TNSRE.2023.3246588
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• Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices
  IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023 40 citations DOI: 10.1109/TNSRE.2023.3237375
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• Electroencephalogram Data Collection for Student Engagement Analysis with Audio-Visual Content
  2022 2 citations DOI: 10.1101/2022.09.20.508656
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• Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network
  IEEE Access 2022 11 citations DOI: 10.1109/ACCESS.2022.3153059
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• A novel machine learning‐based analytical framework for automatic detection of COVID‐19 using chest X‐ray images
  International Journal of Imaging Systems and Technology 2021 26 citations DOI: 10.1002/ima.22613
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• Application of hybrid GLCT-PICA de-noising method in automated EEG artifact removal
  Biomedical Signal Processing and Control 2020 15 citations DOI: 10.1016/j.bspc.2020.101977
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• Application of tunable-Q wavelet transform based nonlinear features in epileptic seizure detection
  Analog Integrated Circuits and Signal Processing 2019 31 citations DOI: 10.1007/s10470-019-01424-y
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