Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex cognitive processes. While the body of evidence about the effect of tACS on behavioral and cognitive performance is constantly growing, those studies fail to address the importance of subjectspecific stimulation protocols. With this study here, we set the foundation to combine tACS with a recently presented framework that utilizes real-time fRMI and Bayesian optimization in order to identify the most optimal tACS protocol for a given individual. While Bayesian optimization is particularly relevant to such a scenario, its success depends on two fundamental choices: the choice of covariance kernel for the Gaussian process prior as well as the choice of acquisition function that guides the search. Using empirical (functional neuroimaging) as well as simulation data, we identified the squared exponential kernel and the upper confidence bound acquisition function to work best for our problem. These results will be used to inform our upcoming realtime experiments.
|Title of host publication
|PRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging
|Institute of Electrical and Electronics Engineers Inc.
|Published - 24 Aug 2016
|6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016 - Trento, Italy
Duration: 22 Jun 2016 → 24 Jun 2016
|6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016
|22/06/2016 → 24/06/2016
- Bayesian optimization
- non-invasive brain stimulation
- transcranial alternating current stimulation