EEG-Based Control of Working Memory Maintenance Using Closed-Loop Binaural Stimulation

The brain is a highly complex network of nonlinear systems with internal dynamic states that are not easily quantified. As a result, it is essential to understand the properties of the connectivity network linking disparate parts of the brain used in complex cognitive processes, such as working memory. Working memory is the system in control of temporary retention and online organization of thoughts for successful goal directed behavior. Individuals exhibit a typically small capacity limit on the number of items that can be simultaneously retained in working memory. To modify network connections and thereby augment working memory capacity, researchers have targeted brain areas using a variety of noninvasive stimulation interventions. However, few existing methods take advantage of the brain's own structure to actively generate and entrain internal oscillatory modulations in locations deep within the auditory pathways. One technique is known as binaural beats, which arises from the brain's interpretation of two pure tones, with a small frequency mismatch, delivered independently to each ear. The mismatch between these tones is perceived as a so-called beat frequency which can be used to modulate behavioral performance and cortical connectivity. Currently, all binaural stimulation therapeutic systems are open-loop "one-size-fits-all" approaches. However, these methods can prove not as effective because each person's brain responds slightly differently to exogenous stimuli. Therefore, the driving motivation for developing a closed-loop stimulation system is to help populations with large individual variability. One such example is persons with mild cognitive impairment (MCI), which causes cognitive impairments beyond those expected based on age. Therefore, applying a closed-loop binaural beat control system to increase the cognitive load level to people with MCI could potentially maintain their quality of life. In this dissertation, I will present a comparison of algorithms to determine brain connectivity, results of open-loop based binaural stimulation, the development of a closed-loop brain network simulation platform, and finally an experimental study to determine the effectiveness of closed-loop control to modulate brain networks hence influencing cognitive abilities.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2018

Author dc:creator

• Beauchene, Christine Elizabeth

Chairs dc:contributor.committeechair

• Abaid, Nicole
• Leonessa, Alexander

Committee members dc:contributor.committeemember

• Moran, Rosalyn J.
• Diana, Rachel A.
• Southward, Steve C.

• Binaural beats
• Electroencephalography
• Neural networks
• Working memory

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:14628

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/83341