Detecting changes in cognitive load through audified EEG

Abstract

Concussion is an increasing concern in society, especially with the popularity of contact sports. New research continues to show the dangers of letting concussions go undetected and untreated. Current assessment methods are lacking in their reliability to detect concussions and track healing. Electroencephalography (EEG) consists of data measured from electrical signals from the brain that can give insight into the activity and health of the brain. It would benefit many to achieve an objective assessment method for concussion. The first step in assessing concussion through EEG is to understand the signal properties while performing different cognitive tasks. While these signals are often displayed graphically, they can be converted to sound (audification) to translate the data into a more intuitive medium. By using EEG to understand how the brain processes information under different levels of cognitive load and interpreting this data through audification, this research can pave the way for audified EEG being used to assess brain health, specifically concussion. Untrained participants were asked to differentiate between high and low cognitive load by listening to audified EEG data relating to different tasks. The data were conveyed in ten-second audio samples; each related to tasks of varying cognitive demand. Eighty-six percent of participants were able to detect the difference between high and low cognitive load, when listening to a total of sixty-four audified samples of EEG data. This study is presented as a proof of concept that shows audified EEG can be used by novices to differentiate between high and low cognitive load. This approach provides initial evidence to support the theory that EEG could be used to objectively detect changes in brain activity due to concussion.