An Adaptable Filter Bank Optimization Method for Evaluating EEG Activation Complexity using Intensity Analysis

Nicholas Napoli1, Matthew Demas1, Chad Stephens2, Kellie Kennedy2, Laura Barnes1

  • 1University of Virginia
  • 2NASA Langley Research Center

Details

09:05 - 09:55 | Fri 17 Feb | Ballroom D | FrRAF.2

Session: Rapid Fire Session 03: Sensor Informatics II

Abstract

Electroencephalograms detect variations in brain wave patterns that provide information about a person's cognitive states and help identify neurological diseases. We overcome time-frequency resolution trade-offs associated with Fourier analysis by using a wavelet filtering method to extract continuous intensity measures of key EEG bands. We used simulated data to validate the method's ability to quantify specific EEG band intensities in continuous time. With this method, we introduced a novel complexity measurement referred to as ``Activation Complexity''. We applied these techniques to 49 subject's EEG data collected under hypoxic and non-hypoxic conditions and identified a significant increase (p< 0.05) in activation complexity within a localized region in the back left side of brain for hypoxic versus non-hypoxic trials.