Brain Computer Interfaces (BCI)
We are developing brain computer interfaces (BCIs) which opportunistically measure neural correlates of attentional shift, arousal, engagement and recognition that are used to construct labels and/or reinforcement signals for machine learning systems.
Computational Network Models
We use computational modeling to link our macroscopic observations/findings from our human neuroimaging work to mesoscopic scale neuronal population activity and circuity.
Machine Learning (ML)
At the core of much of our research is machine learning (ML) for predicting, classifying and fusing multiple neural and non-neural data streams to better identify the cortical and subcortical networks underlying rapid decision making.
Neuroimaging
Our group uses structural and functional neuroimaging, including EEG, fMRI, DTI and fNIRS, to identify the neural constructs underlying cognition and behavior, particularly within the context of rapid decision making.