Iyad Obeid

Iyad Obeid

  • College of Engineering

    Electrical and Computer Engineering

    Associate Professor

Office Location

Engineering Room 719

Direct

215-204-9033

Department

215-204-7597

Biography

Dr. Obeid is an Associate Professor of Electrical and Computer Engineering with a secondary appointment in Bioengineering. His research focuses on neural and biomedical signal processing. Applications include brain computer interfaces, traumatic brain injury, virtual reality for cognitive and physical therapy, and neurofeedback.

Dr. Obeid is a recipient of the National Science Foundation CAREER Award as well as a Lindback Award for distinguished teaching. He has been funded by NSF, NIH, and DARPA, amongst others.

Labs: Neural Instrumentation Lab

Research Interests

  • Neural engineering
  • Neural signal processing
  • Biomedical signal processing
  • Biomedical data analytics
  • Neurofeedback

Courses Taught

Number

Name

Level

ECE 3822

Engineering Computation II

Undergraduate

ECE 3824

Engineering Computation III

Undergraduate

ECE 5033

Probability and Random Processes

Graduate

ENGR 2011

Engineering Analysis & Applications

Undergraduate

ENGR 9185

Experience in Engineering Profession I

Graduate

Selected Publications

Featured

  • Shawki, N., Elseify, T., Cap, T., Shah, V., Obeid, I., & Picone, J. (2020). A Deep Learning-Based Real-time Seizure Detection System. 2020 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2020 - Proceedings. doi: 10.1109/SPMB50085.2020.9353623

  • Kane, Z., Stecco, E., Napoli, A., Tucker, C., & Obeid, I. (2019). The Instrumented Multitask Assessment System (IMAS). 2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2019 - Proceedings. doi: 10.1109/SPMB47826.2019.9037841

  • Hunt, I., Husain, S., Simon, J., Obeid, I., & Picone, J. (2019). Recent Advances in the Temple University Digital Pathology Corpus. 2019 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2019 - Proceedings. doi: 10.1109/SPMB47826.2019.9037859

  • Glass, S.M., Napoli, A., Thompson, E.D., Obeid, I., & Tucker, C.A. (2019). Validity of an Automated Balance Error Scoring System. J Appl Biomech, 35(1), pp. 32-36. United States. doi: 10.1123/jab.2018-0056

  • Houser, D., Shadhin, G., Anstotz, R., Campbell, C., Obeid, I., Picone, J., Farkas, T., Persidsky, Y., & Jhala, N. (2019). The Temple University Hospital Digital Pathology Corpus. 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings. doi: 10.1109/SPMB.2018.8615619

Recent

  • Han, I., Obeid, I., & Greco, D. (2023). Multimodal Learning Analytics and Neurofeedback for Optimizing Online Learners’ Self-Regulation. Technology, Knowledge and Learning, 28(4), pp. 1937-1943. Springer Science and Business Media LLC. doi: 10.1007/s10758-023-09675-5

  • Roy, S., Kiral, I., Mirmomeni, M., Mummert, T., Braz, A., Tsay, J., Tang, J., Asif, U., Schaffter, T., Ahsen, M.E., Iwamori, T., Yanagisawa, H., Poonawala, H., Madan, P., Qin, Y., Picone, J., Obeid, I., Marques, B.D.e.A., Maetschke, S., Khalaf, R., Rosen-Zvi, M., Stolovitzky, G., Harrer, S., & Consortium, I.E. (2021). Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data. EBioMedicine, 66, p. 103275. Netherlands. doi: 10.1016/j.ebiom.2021.103275

  • Hamid, A., Gagliano, K., Rahman, S., Tulin, N., Tchiong, V., Obeid, I., & Picone, J. (2020). The Temple University Artifact Corpus: An Annotated Corpus of EEG Artifacts. 2020 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2020 - Proceedings. doi: 10.1109/SPMB50085.2020.9353647

  • Shah, V., Obeid, I., Picone, J., Ekladious, G., Iskander, R., & Roy, Y. (2020). Validation of Temporal Scoring Metrics for Automatic Seizure Detection. 2020 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2020 - Proceedings. doi: 10.1109/SPMB50085.2020.9353631

  • Campbell, C., Mecca, N., Duong, T., Obeid, I., & Picone, J. (2019). Expanding an HPC Cluster to Support the Computational Demands of Digital Pathology. 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings. doi: 10.1109/SPMB.2018.8615614

  • Capp, N., Campbell, C., Elseify, T., Obeid, I., & Picone, J. (2019). Optimizing EEG Visualization Through Remote Data Retrieval. 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings. doi: 10.1109/SPMB.2018.8615613

  • Shah, V., Anstotz, R., Obeid, I., & Picone, J. (2019). Adapting an Automatic Speech Recognition System to Event Classification of Electroencephalograms 1. 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings. doi: 10.1109/SPMB.2018.8615625

  • Ferrell, S., Weltin, E.V., Obeid, I., & Picone, J. (2019). Open Source Resources to Advance EEG Research. 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings. doi: 10.1109/SPMB.2018.8615622

  • Golmohammadi, M., Ziyabari, S., Shah, V., Obeid, I., & Picone, J. (2019). Deep Architectures for Spatio-Temporal Modeling: Automated Seizure Detection in Scalp EEGs. Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, pp. 745-750. doi: 10.1109/ICMLA.2018.00118

  • Golmohammadi, M., Torbati, A.H.H.N., Diego, S.L.d.e., Obeid, I., & Picone, J. (2019). Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures. Front Hum Neurosci, 13, p. 76. Switzerland. doi: 10.3389/fnhum.2019.00076

  • Ward, C. & Obeid, I. (2019). Application of identity vectors for EEG classification. J Neurosci Methods, 311, pp. 338-350. Netherlands. doi: 10.1016/j.jneumeth.2018.09.015