| Autor: |
Napoli NJ; Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611, United States. n.napoli@ufl.edu.; National Institute of Aerospace, Hampton, VA, 23681, United States. n.napoli@ufl.edu.; University of Florida, Dept. of Electrical and Computer Engineering, Gainesville, FL, 32611, United States. n.napoli@ufl.edu., Demas M; Systems and Information Engineering, University of Virginia, Charlottesville, VA, 22904, United States., Stephens CL; NASA Langley Research Center, Hampton, VA, 23681, United States., Kennedy KD; NASA Langley Research Center, Hampton, VA, 23681, United States., Harrivel AR; NASA Langley Research Center, Hampton, VA, 23681, United States., Barnes LE; Systems and Information Engineering, University of Virginia, Charlottesville, VA, 22904, United States., Pope AT; NASA Langley Research Center, Hampton, VA, 23681, United States. |
| Abstrakt: |
Electroencephalography (EEG) is a method for recording electrical activity, indicative of cortical brain activity from the scalp. EEG has been used to diagnose neurological diseases and to characterize impaired cognitive states. When the electrical activity of neurons are temporally synchronized, the likelihood to reach their threshold potential for the signal to propagate to the next neuron, increases. This phenomenon is typically analyzed as the spectral intensity increasing from the summation of these neurons firing. Non-linear analysis methods (e.g., entropy) have been explored to characterize neuronal firings, but only analyze temporal information and not the frequency spectrum. By examining temporal and spectral entropic relationships simultaneously, we can better characterize how neurons are isolated, (the signal's inability to propagate to adjacent neurons), an indicator of impairment. A novel time-frequency entropic analysis method, referred to as Activation Complexity (AC), was designed to quantify these dynamics from key EEG frequency bands. The data was collected during a cognitive impairment study at NASA Langley Research Center, involving hypoxia induction in 49 human test subjects. AC demonstrated significant changes in EEG firing patterns characterize within explanatory (p < 0.05) and predictive models (10% increase in accuracy). The proposed work sets the methodological foundation for quantifying neuronal isolation and introduces new potential technique to understand human cognitive impairment for a range of neurological diseases and insults. |
