John Thomas

John Thomas

Neuroengineering Scientist at Duke University:Biomedical Signal Processing | Deep Learning | Epilepsy | Sleep | Brain-Computer Interfaces

Brain Disorders

This theme focuses on understanding various neurological disorders through innovative diagnostic tools, optimizing surgical outcomes, and exploring advanced neuroimaging and computational techniques.

Epilepsy Diagnosis

Advancing the diagnosis of epilepsy by leveraging EEG data and machine learning to develop more accurate and automated diagnostic tools.

  • P. Thangavel, J. Thomas (equal contribution), N. Sinha, et al. (2022). Improving automated diagnosis of epilepsy from EEGs beyond IEDs. Journal of Neural Engineering, 19(6), 066017. https://doi.org/10.1088/1741-2552/ac9c93

  • R. Yuvaraj, J. Thomas, E. Bagheri, J. Dauwels, R. Rathakrishnan, and Y. L. Tan (2022). Computational Approaches for Diagnosis and Monitoring of Epilepsy from Scalp EEG. Springer, pp. 1–31.

  • J. Thomas, P. Thangavel, W. Y. Peh, et al. (2021). Automated adult epilepsy diagnostic tool based on interictal scalp electroencephalogram characteristics: A six-center study. International Journal of Neural Systems, 31(05), 2050074. https://doi.org/10.1142/S0129065720500744

  • J. Thomas, J. Jin, P. Thangavel, et al. (2020). Automated detection of interictal epileptiform discharges from scalp electroencephalograms by convolutional neural networks. International Journal of Neural Systems, 30(11), 2050030. https://doi.org/10.1142/S0129065720500306

Epilepsy Surgery

Enhancing surgical strategies for epilepsy, focusing on identifying epileptogenic zones and optimizing patient outcomes.

  • J. Thomas, C. Abdallah, Z. Cai, et al. (2024). Investigating current clinical opinions in stereoelectroencephalography-informed epilepsy surgery. Epilepsia.

  • C. Abdallah, J. Thomas, …, and B. Frauscher (2024). Visual features in stereo-electroencephalography to predict surgical outcome: A multicenter study. Annals of Neurology, under review.

  • W. Shi, D. Shaw, …, J. Thomas, et al. (2024). Spike ripples localize the epileptogenic zone best: An international intracranial study. Brain, awae037.

  • J. Thomas, P. Kahane, C. Abdallah, et al. (2023). A subpopulation of spikes predicts successful epilepsy surgery outcome. Annals of Neurology, 93(3), 522–535. https://onlinelibrary.wiley.com/doi/full/10.1002/ana.26548

Characterization and Phenotyping

Refining the characterization and classification of neurological disorders through neuroimaging and computational modeling.

  • J. Thomas, C. Abdallah, K. Jaber, V. Latreille, L. Minotti, et al. (2024). Development of a seizure matching system for clinical decision making in epilepsy surgery. Journal of Neural Engineering, in press.

  • S. Abirami, Y. Rajamanickam, R. Menon, J. Thomas, and A. R. Jac Fredo (2024). Characterization and classification of seizure types using time-frequency decomposition of scalp EEG signals. IEEE Access, under review.

  • K. Schiller, J. Thomas, T. Avigdor, et al. (2024). Pulsatile corticoid therapy reduces interictal epileptic activity burden in children with genetic drug-resistant epilepsy. Epilepsia Open.

  • S. G. AA, D. Vedantham, J. Thomas, …, and A. R. JF (2023). Optimization of pre-ictal interval time period for epileptic seizure prediction using temporal and frequency features. Studies in Health Technology and Informatics, 302, 232–236. https://europepmc.org/article/med/37203653

Stimulation

Investigating the use of non-invasive stimulation techniques to modulate brain activity and improve outcomes for neurological disorders.

  • T. Arafat, J. Thomas, G. Gurevitch, …, F. Fahoum, and S. Kipervasser (2024). Music therapy’s potential to mitigate epileptic activity in drug-resistant focal epilepsy. Accepted for presentation at the Canadian League Against Epilepsy (CLAE) Annual Meeting.

Autism Diagnosis

Advancing the diagnosis of autism through innovative neuroimaging techniques and machine learning models.

  • J. F. A. Ronicko, J. Thomas, P. Thangavel, V. Koneru, G. Langs, and J. Dauwels (2020). Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation. Journal of Neuroscience Methods, 345, 108884. https://doi.org/10.1016/j.jneumeth.2020.108884