Full Wave Modeling of Brain Waves as Electromagnetic Waves (Invited Paper)

(Invited Paper) Abstract—This paper describes a novel technique which has the potential to make a significant impact on the mapping of the human brain. This technique has been designed for 3D full-wave electromagnetic simulation of waves at very low frequencies and has been applied to the problem of modeling of brain waves which can be modeled as electromagnetic waves lying in the frequency range of 0.1-100 Hz. The use of this technique to model the brain waves inside the head enables one to solve the problem on a regular PC within 24 hrs, and requires just 1 GB of memory, as opposed to a few years of run time and nearly 200 Terabyte (200,000 GB) needed by the conventional FDTD (Finite Difference Time Domain) methods. The proposed technique is based on scaling the material parameters inside the head and solving the problem at a higher frequency (few tens of MHz) and then obtaining the actual fields at the frequency of interest (0.1-100 Hz) by using the fields computed at the higher frequency. The technique has been validated analytically by using the Mie Series solution for a homogeneous sphere, as well as numerically for a sphere, a finite lossy dielectric slab and the human head using the conventional Finite Difference Time Domain (FDTD) Method. The presented technique is universal and can be used to obtain full-wave solution to low-frequency problems in electromagnetics by using any numerical technique.

[1]  P. Hoole,et al.  Autism, EEG and brain electromagnetics research , 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

[2]  V. Jandhyala,et al.  Solving low frequency EM-CKT problems using the PEEC method , 2005, IEEE 14th Topical Meeting on Electrical Performance of Electronic Packaging, 2005..

[3]  C Gabriel,et al.  The dielectric properties of biological tissues: I. Literature survey. , 1996, Physics in medicine and biology.

[4]  W. Steen Absorption and Scattering of Light by Small Particles , 1999 .

[5]  Akihiro Yasuhara,et al.  Correlation between EEG abnormalities and symptoms of autism spectrum disorder (ASD) , 2010, Brain and Development.

[6]  R. W. Lau,et al.  The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. , 1996, Physics in medicine and biology.

[7]  Yoshitsugu Yasui,et al.  A brainwave signal measurement and data processing technique for daily life applications. , 2009, Journal of physiological anthropology.

[8]  M. Samet,et al.  Parametric study on the dielectric properties of biological tissues , 2015, 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[9]  N. Ponomareva,et al.  EEG Alterations in Subjects at High Familial Risk for Alzheimer’s Disease , 2003, Neuropsychobiology.

[10]  Brian N. Pasley,et al.  Reconstructing Speech from Human Auditory Cortex , 2012, PLoS biology.

[11]  P Suppes,et al.  Brain-wave representation of words by superposition of a few sine waves. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[12]  K. Müller,et al.  Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes , 2007, PloS one.

[13]  M. Baggen,et al.  DOUBLE‐BLIND STUDY WITH PHOSPHATIDYLSERINE IN PARKINSONIAN PATIENTS WITH SENILE DEMENTIA OF ALZHEIMER'S TYPE (SDAT) , 1989, Progress in clinical and biological research.

[14]  A. Doud,et al.  Continuous Three-Dimensional Control of a Virtual Helicopter Using a Motor Imagery Based Brain-Computer Interface , 2011, PloS one.

[15]  Dan Jiao,et al.  A unified finite-element solution from zero frequency to microwave frequencies for full-wave modeling of large-scale three-dimensional on-chip interconnect structures , 2008, 2008 IEEE Antennas and Propagation Society International Symposium.

[16]  C. Grozea,et al.  Bristle-sensors—low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications , 2011, Journal of neural engineering.

[17]  K. Asami Dielectric properties of biological tissues in which cells are connected by communicating junctions , 2007 .

[18]  K. Lafleur,et al.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.

[19]  C. Gabriel The Dielectric Properties of Tissues , 2000 .

[20]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[21]  S. O. Nelson,et al.  Low-frequency dielectric properties of biological tissues : A review with some new insights , 1998 .

[22]  Warren M Grill,et al.  Analysis of the quasi-static approximation for calculating potentials generated by neural stimulation , 2008, Journal of neural engineering.