Moses obtained his PhD degree in Medical Imaging in January 2012 at the Aberdeen Biomedical Imaging Centre, University of Aberdeen, UK. He obtained the Bachelor's degreee. and Master's degree in Electronic and Electrical Engineering at the Obafemi Awolowo University (OAU), Ile-Ife, Nigeria in 2000 and 2005 respectively. He was a member of the faculty of Technology and Lecturer at the Department of Electronic and Electrical Engineering, OAU, Ile-Ife, Nigeria where he taught undergraduate Telecommunication courses. He was also a postdoctoral research associate in rtfMRI brain-computer interfacing (BCI) at Cardiff University, UK. He is presently a postdoctoral researcher at SISSA. His research interests include biomedical signal and image processing, BCI and facial-feedback device.
Publications:
Ihssen N, Sokunbi MO, Lawrence AD, Lawrence NS, Linden DEJ. (2016). Neurofeedback of visual food cue reactivity: a potential avenue to alter incentive sensitization and craving. Brain Imaging and Behavior. DOI 10.1007/s11682-016-9558-x
Habes, I., Rushton, S., Johnston, S.J., Sokunbi, M.O., Barawi, K., Brosnan, M., Daly T., Ihssen N., Linden, D.E.J., (2016) “fMRI neurofeedback of higher visual areas and perceptual biases”, Neuropsychologia, http://dx.doi.org/10.1016/j.neuropsychologia.2016.03.031, “In press”
Cohen Kadosh, K., Luo, Q., de Burca, C., Sokunbi, M.O., Feng, J., Linden, D.E.J., Lau, J.Y.F. (2016). Using real-time fMRI to influence effective connectivity in the developing emotion regulation network. NeuroImage doi:10.1016/j.neuroimage.2015.09.070.
Sokunbi, M.O., Cameron, G.G., Ahearn, T.S., Murray, A.D., Staff, R.T., (2015), Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span. Medical Engineering and Physics. Med Eng Phys. 2015 Nov; 37(11):1082-90. Doi: 10.1016/j.medengphy.2015.09.001.
Atijosan, A., Adeniran, S.A., Sokunbi, M.O., Badru, R. (2015). Development of a Technique for Restoring the Fidelity of Distorted Playback Audio Signal from Analog Cassette Tape. British Journal of Applied Science & Technology; 9(4): 338-345, 2015, Article no.BJAST.2015.273, ISSN: 2231-0843.
Sokunbi, M.O., Linden, D.E.J., Habes, I., Johnston, S., Ihssen, N., (2014). Real-time fMRI brain-computer interface: Development of a “motivational feedback” subsystem for the regulation of visual cue reactivity. Frontiers in Behavioral Neuroscience, 8 (10.3389/fnbeh.2014.00392).
Sokunbi, M.O., (2014). Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets. Front. Neuroinform. 8:69. doi: 10.3389/fninf.2014.00069.
Sokunbi, M.O, Gradin, V.B, Waiter, G.D., Cameron, G.G, Ahearn, T.S., Murray, A.D, Steele, D.J., Staff, R.T. (2014). Nonlinear complexity analysis of brain fMRI signals in schizophrenia. PLoS ONE 9(5): e95146. doi:10.1371/journal.pone.0095146
Sokunbi, M.O., Fung, W., Sawlani, V., Choppin, S., Linden, D.E.J, Thome, J., (2013). Resting state fMRI entropy probes complexity of brain activity in adults with ADHD. Psychiatry Research: Neuroimaging 214: 341 – 348.
Sokunbi, M.O., Staff, R.T., Waiter, G.D., Ahearn, T.S., Fox, H.C., Deary, I.J., Starr, J.M., Whalley, L.J. & Murray, A.D. (2011). Inter-individual Differences in fMRI Entropy Measurements in Old Age. IEEE transactions on bio-medical engineering, vol. 58, no. 11, pp. 3206-14.
Sokunbi, M.O. (2006). Installing a 3 node Linux based cluster for scientific computation”, International Journal HIT Transactions on ECCN, ISSN: 0973-6875, Vol. 1, No. 4: 192 – 204.
