The goal of this thesis is to design methods and techniques for closed-loop transcranial Electric Stimulation (tES) based on feedback from ongoing neural activity. tES is a popular Non-Invasive Brain Stimulation (NIBS) approach and has been shown to modulate ongoing brain activity to affect behaviour. The application of these methods has major implications in both understanding the anatomy & function of the brain, and development of therapies for non-pharmacological intervention in mental health disorders. A review of existing brain imaging techniques compatible with tES shows that electroencephalography (EEG) is the most suitable method to pair with tES to design the closed-loop stimulation interface. Furthermore, of the different forms of tES, transcranial Alternating Current Stimulation (tACS) is the ideal method to use for closed-loop stimulation due to its ability to modulate ongoing neural activity in a phase specific manner. However, most studies have been limited to exploring changes in EEG before and after stimulation due to the presence of stimulation artifacts in the EEG data. A characterisation of these artifacts and a review of existing methods will identify the shortcomings of current practices. Thus, two novel algorithms for tACS artifact removal are be presented. Further, methods to judge the performance of such algorithms are currently limited and thus, new techniques to comprehensively test and verify these algorithms are presented, including the use of a novel phantom head model. A proof of concept for assessment of EEG activity during tACS is presented using novel methods that allow monitoring working memory during stimulation, via successful classification of data during two different tasks. This presents a novel technique to both verify artifact removal and also monitor ongoing neural activity during stimulation. Finally, an interface that executes the developed techniques in real-time was built. This toolbox is subsequently capable of providing closed-loop feedback to adjust tACS parameters based on ongoing EEG activity. It was designed to be as independent of hardware as possible, making it easy for other researchers to employ this toolbox in different labs across the world. This will allow for easier repeatability of methods in the field of tES research, which is a known issue. In summary, the novel techniques presented in this thesis are key steps towards development of closed-loop tACS as a tool for personalised, nonpharmacological therapy in mental health disorders.
|Date of Award||1 Aug 2018|
- The University of Manchester
|Supervisor||David Foster (Supervisor) & Alex Casson (Supervisor)|