Understanding How the Human Brain Functions in Everyday Life Has Always Been a Complex

References

Understanding how the human brain functions in everyday life has always been a complex problem. Recent developments in technology have made it easier to use electroencephalography (EEG) in less restricted settings. For example, the uses of small portable EEG amplifiers, approximately the size of a large coin, have the potential to liberate researchers from the confines of a laboratory. This would allow scientists to understand how the brain functions in a more natural setting. In addition to new equipment, researchers have begun using natural stimulus when studying the brain. In recent studies using functional magnetic resonance imaging (fMRI) and EEG, participants were presented with music and video clips in order to explore the correlation in brain activity between subjects listening to the same piece of music or watching the same video (Dmochowski et. al., 2012, Nummenmaa et. al., 2012). Using these new technologies and techniques, scientists will come closer to isolating the brain’s response to complex stimuli.

In a typical EEG experiment, in which participants are asked to remain as still as possible; there are always various forms of experimental noise present. Some of this noise is caused by muscle or eye movements and is present across many electrodes over a range of different frequencies (Silberstein, 1995). Already, researchers have used wavelets to remove noise from EEG data with great success (Quiroga, 2000, Vázquez et. al., 2012). However, as portable EEG technology and new experimental paradigms are being used, noise resulting from complex environmental stimulus will be present. Again, the use of wavelets may help to isolate and filter this noise out (Mallet, 2008). I propose a project to explore the application of wavelets on EEG data gathered using a natural stimulus paradigm.

I believe this project would be a valuable experience in providing me with hands on practice using wavelets to clean and analyze EEG data, as well as, an understanding of applied math research. This semester I will be taking the Fourier analysis and wavelets course instructed by Dr. Pereyra. While I’m learning about Fourier analysis and wavelets I would have the opportunity to use the skills gained from this course. This would add hands on experience, enriching my learning. This project will also provide me with an understanding of applied math research and the tenacity needed to complete such research. Ultimately this project will allow me to make an informed decision when applying to graduate school and my future career plans.

Dmochowski, J.P., Sajda, P., Dias, J., and Parra, L.C. (2012). “Correlated components of ongoing EEG point to emotionally laden attention –a possible marker of engagement?,” in Front. Human. Nurosci. 6:112. doi: 10.3389/fnhum.2012.00112.

Mallat, S. (2008). “A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way” Academic Press, MA, USA.

Nummenmaa, L., Glerean, E., Viinikainen, M., Jääskeläinen, I.P., Hari, R., and Sams, M. (2012). “Emotions promote social interaction by synchronizing brain activity across individuals,” in PNAS Early Edition, 10:1073. pnas.1206095109.

Quiroga, R.Q. (2000). “Obtaining single stimulus evoked potentials with wavelet denoising” in Physica D: Nonlinear Phenomena, 145:3.

Silberstein, R.B. (1995). “Steady state visually evoked potentials, brain resonances and cognitive processes,” in Neocortical Dynamics and Human EEG Rhythms, ed P.

Vázquez, R.R., Vélez-Pérez, H., Ranta, R., Louis, V.D., Maquin, D., and Maillard, L. (2012). “Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling” in Biomedical Signal Processing and Control, 7:4.