Hojjat Adeli, PhD

Department of Biomedical Informatics

Adeli_Hojjat_460x460Professor

College of Medicine
College of Engineering
Department of Biomedical Informatics
Civil and Environmental Engineering and Geodetic Science

Faculty Affiliate, Chronic Brain Injury

614-292-7929
Adeli.1@osu.edu

Research Interests

  • Computational neuroscience
  • Biological and brain signal processing
  • Computational modeling of brain and neurological disorders such as epilepsy, Alzheimer's disease, Parkinson's disease, autism spectrum disorder, stroke, ADHD
  • Memory and learning
  • Wavelets

College of Engineering profile

Publications

  •  Adeli, H. and Ghosh-Dastidar, S., Automated EEG-based Diagnosis of Neurological Disorders - Inventing the Future of Neurology, CRC Press, Taylor & Francis, Boca Raton, Florida, 2010.
  • Ortiz-Rosario, A. Adeli, H. and Buford, J.A. (2015), “Wavelet Methodology to Improve Single Unit Isolation in Primary Motor Cortex Cells,” Journal of Neuroscience Methods, 246, 106-118. 
  • Acharya,  U.R., Vidya S., Adeli,  H., Jayashree, S., Koh, J.E.W., and Adeli, A. (2015), “Computer Aided Diagnosis of Depression Using EEG Signals,” European Neurology, 73, 329-336.
  • Acharya,  U.R., Vidya S., Adeli,  H., Jayashree, S., Koh, J.E.W., and Adeli, A. (2015), “A Novel Depression Diagnosis Index Using Nonlinear Features in EEG Signals,” European Neurology, 74(1-2), pp. 79-83.
  • Hirschauer, T., Adeli, H., and Buford, T. (2015), “Computer-Aided Diagnosis of Parkinson’s Disease using an Enhanced Probabilistic Neural Network,” Journal of Medical Systems, 39:179, (12 pages).
  • Hulbert, H. and Adeli, H (2015)., “Spotting Psychopaths Using Technology,” Reviews in the Neurosciences,” 26:6, 721-732 (DOI: 10.1515/revneuro-2015-0025).
    Bhat, S., Acharya, U.R., Dadmehr, and Adeli, H. (2015), “Clinical neuro-physiological and automated EEG-based diagnosis of the Alzheimer’s disease,” European Neurology, 74, 202-210. 
  • Acharya, U.R., Bhat, S., Faust, O., Adeli, H., Chua, E.C.P., W.J.E. Lim, and Koh, J.E.W. (2015), “Nonlinear Dynamics Measure for Automated EEG-based Sleep Stage Detection,” European Neurology, 74, 268-287 (DOI:10.1159/000441975).
  • Yuvaraj, R., Murugappan, M., Sundaraj, K., Omar, M.I., Ibrahim, N.M., Mohamad, K., Palaniappan, R., Acharya, U.R., Adeli, H., and Mesquita, E. (2016), “Brain Functional  Connectivity Patterns for Emotional State Classification in Parkinson’s Disease Patients Without Dementia,” Behavioural Brain Research, 298, 2016, 248-260.
  • Amezquita-Sanchez, J.P., Adeli, A., and Adeli, H. (2016), “A New Methodology for Automated Diagnosis of Mild Cognitive Impairment (MCI) using Magnetoencephalography (MEG),” Behavioural Brain Research, 305, pp. 174-180.
  • Mirzaei, G., Adeli, A., and Adeli, H., “Imaging and Machine Learning Techniques for Diagnosis of Alzheimer Disease,” Reviews in the Neurosciences, 27:8, 2016, pp. 857-870.
  • Mirzaei, G. and Adeli, H., “Resting State Functional Magnetic Resonance Image Processing Techniques in Stroke Studies”, Reviews in the Neurosciences, 27:8, 2016, pp. 871-885.
  • Ahmadlou, M. and Adeli, H., “Complexity of Weighted Graph: A New Technique to Investigate Structural Complexity of Brain Activities with Applications to Aging and Autism,” Neuroscience Letters, 650, 2017, pp. 103-108. 
  • George, S.H., Rafiei, M.H., Borstad, A., Gauthier, L., Buford, J.A., and Adeli, H. “Computer-Aided Prediction of Extent of Motor Recovery Following Constraint-Induced Movement Therapy in Chronic Stroke,” Behavioural Brain Research, 327, 2017, 191-199 (10.1016/j.bbr.2017.03.012).

Education

PhD: Stanford University

Honors and News

  • Editor-in-Chief, International Journal of Neural Systems

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