Analysis Of Basal EEG Potentials To Find Evidence For Power Law Distributions

The collective activity of large parts of the brain can be recorded using surface electrodes through a technique known as electroencephalography. Electroencephalograms (EEG) record real-time brain activity that is then correlated with behavioral tasks. Traditionally, the subject receives a stimulus and the EEG activity before the physical reaction is correlated with the performance of the test. Due to learning and experience, some individuals are expected to perform better than others. Currently, there are no available data analysis techniques that measure the relationship between the basal EEG and the subsequent occurrence of local field potential spikes. Our hypothesis is that the basal EEG activity contains information that can be useful in predicting the performance of individuals before a test. We will analyze EEG responses of balanced and unbalanced bilinguals performing a Stroop test. We will calculate the histogram of values in the temporal and frequency domains using Matlab routines. It has been suggested that EEG activity should follow a power law distribution when the brain matures and is enriched by learning and experience. Thus, we predict that basal EEG that show power law distributions will perform better at the Stroop test than those that show exponential or Gaussian distributions. Partially supported by the UTSA CRTS SURF program, NSF-EF 1137897, and NICHD/NIGMS HD060435.