People > Faculty

Ceon Ramon


Ceon Ramon
Affiliate Professor
Modeling of Electrical Activity of Human Brain
Analysis of High Density (256-Channel) EEG data
Application to Epilepsy and Human Cognition Models
Forward and Inverse modeling of EEG/MEG data

206M EEB
Box 352500
University of Washington
Seattle, WA 98195
Cell Phone: (206) 778.0942
E-mail:

PhD 1973 University of Utah
B.E. (Hon) 1966 Indian institute of Science, Bangalore


Biosketch

Dr. Ceon Ramon’s Ph.D. thesis work was in lasers and quantum optics. After graduation, his research interests shifted to biomedical engineering; primarily focused on the electrical activity of the human heart and brain. Since 1990, his research efforts have been largely involved with neuroscience and neurodynamics. He is currently involved in computer modeling of the electrical activity of human brain under normal and diseased conditions and developing newer technologies for noninvasive electrical stimulation of the brain to treat epilepsy and stroke. Previously, he has held research and teaching positions at SUNY/Stony Brook, Seattle University and at the University of Washington. Presently, he is an Affiliate Professor of Electrical Engineering at the University of Washington and a Professor of Biomedical Engineering at Reykjavik University, Iceland.

Honors

Memberships: Eta Kappa Nu, Sigma Xi, Bioelectromagnetic Society (BEMS) 1981-98, Institution of Electrical and Electronics Engineers (IEEE), International Society of Bioelectric Topography (ISBET), URSI National Radio Science; Fellow of Institution of Electrical and Telecommunications Engineers (IETE). Member of NIH and NSF proposal review panels. Have also reviewed proposals for the U.S. Department of Energy.

Research Highlights

Human Head Models

An example of the segmentation and 3-D reconstruction of tissue surfaces are given here. Most of the major tissue surfaces are identified in each slice. These include: scalp, hard and soft skull bone, gray and white matter, CSF (cerebrospinal fluid), cerebellum etc. In general, we identify 19 different tissue types. These models are used for computer modeling of the electrical activity of human brain.


Fig. 1. (A) Raw MR slice, (B) segmented slice, (C) 3-D tissue surfaces and (D) 3-D skull bone structure.

Phase Clusters in Epilepsy Localization


Fig. 2. High density EEG data collection

Fig. 2 shows 256-channel high density EEG data collection. It is often called dEEG data. Phase information can be derived from the EEG data using Hilbert transform. Spatiotemporal dynamics of EEG phase is very similar to the formation of bubbles in boiling water. These exhibit phase and amplitude modulated waves. Stronger and stable phase patterns were observed in the seizure area as compared with nearby surrounding brain areas. A clustering of spatial patterns was also observed which was denser in the seizure areas as compared with nearby surrounding areas. These preliminary results show that the spatiotemporal dynamics and clustering of phase cone patterns have a potential to localize the epileptic zones from the scalp dEEG data.


Fig. 3. Contour plots of stable phase cluster rate (frames/sec). The seizure onset area is marked as a rectangle. The rate is higher in and in the vicinity of the seizure onset area.

Student Projects

Short projects of 3-6 months duration are available for B.S. and M.S. students. Long term projects are also available for Ph.D. students.

Selected Publications (out of 170)

  1. Schimpf PH, Liu H, Ramon C, Haueisen J (2005) Efficient electromagnetic source imaging with adaptive standardized LORETA/FOCUSS. IEEE Trans Biomed Eng 52: 901-908.
  2. Ramon C, Haueisen J, Schimpf PH (2006) Influence of head models on neuromagnetic fields and inverse source reconstructions. BioMedical Engineering OnLine 5(1): 55. [PMCID: PMC1629018] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1629018/
  3. Ramon C, Schimpf PH, Haueisen J (2006) Influence of head models on EEG simulations and inverse source localization. BioMedical Engineering OnLine 5: 10. [PMCID: PMC1389789] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1389789/
  4. Ramon C, Holmes MD, Freeman WJ, McElroy R, Rezvanian E (2008) Comparative analysis of temporal dynamics of EEG and phase synchronization of EEG to localize epileptic sites from high density scalp EEG interictal recordings. Conf Proc IEEE Eng Med Biol Soc. 2008. 4548-4550.
  5. Ramon C, Holmes M, Freeman WJ, Gratkowski M, Eriksen KJ, Haueisen J (2009) Power spectral density changes and language lateralization during covert object naming tasks measured with high-density EEG recordings. Epilepsy Behav 14: 54-59. http://www.ncbi.nlm.nih.gov/pubmed/18790081
  6. Ramon C, Freeman WJ, Holmes M, Ishimaru A, Haueisen J, Schimpf PH, Rezvanian E (2009) Similarities between simulated spatial spectra of scalp EEG, MEG and structural MRI. Brain Topogr 22(3): 191-196. [PMCID: PMC2749166] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2749166
  7. Freeman WJ, Ramon C, Holmes MD (2010) 1-D spatial autocorrelation function of EEG: a sensitive assay of occult EMG and a tool to treat it. Abstract #881. HBM2010; 16th Annual Meeting of the Organization for Human Brain Mapping; June 6-10, 2010, Barcelona, Spain.
  8. Ramon C, Holmes MD (2012) Noninvasive epileptic seizure localization from stochastic behavior of short duration interictal high density scalp EEG data. Brain Topogr 25(1): 106-15.
  9. Friðgeirsson E A , Gargiulo P, Ramon C, Haueisen J (2012) 3D segmented model of head for modelling electrical activity of brain. European Journal Translational Myology - Basic Applied Myology 22 (1&2): 57-60. http://www.bio.unipd.it/bam/PDF/22-1&2/Gargiulo.pdf
  10. Ramon C, Holmes MD (2013) Noninvasive Localization of Epileptic Sites from Stable Phase Synchronization Patterns on Different Days Derived from Short Duration Interictal Scalp dEEG. Brain Topogr 26(1): 1-8.
  11. Ramon C, Holmes MD (2013). Stochastic Behavior of Phase Synchronization Index and Cross-frequency Couplings in Epileptogenic Zones During Interictal Periods Measured with Scalp dEEG. Front. Neurol. 4:57. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3655632/
  12. Ramon C, Holmes MD, Freeman WJ (2013) Increased Phase Clustering in Epileptogenic Areas Measured with 256-Channel Dense Array EEG. J Neurol Transl Neurosci 2(1): 1029. http://www.jscimedcentral.com/Neuroscience/neuroscience-2-1029
  13. Odabaee M, Freeman WJ, Colditz PB, Ramon C, Vanhatalo S (2013) Spatial patterning of the neonatal EEG suggests a need for a high number of electrodes. Neuroimage 68: 229-35. http://www.ncbi.nlm.nih.gov/pubmed/23246993
  14. Odabaee M, Tokariev A, Layeghy S, Mesbah M, Colditz PB, Ramon C, Vanhatalo S. (2014) Neonatal EEG at scalp is focal and implies high skull conductivity in realistic neonatal head models. Neuroimage 2014.04.007. [Epub ahead of print] http://www.ncbi.nlm.nih.gov/pubmed/24736169
  15. Ramon C, Garguilo P, Fridgeirsson EA, Jens Haueisen J. (2014) Changes in Scalp Potentials and Spatial Smoothing Effects of Inclusion of Dura Layer in Human Head Models for EEG Simulations. Frontiers in Neuroengineering. In review.

People  
EE logo