Jonathan Lisinski

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Brain-computer interfaces (BCIs) can convert mental states into signals to drive real-world devices, but it is not known if a given covert task is the same when performed with and without BCI-based control. Using a BCI likely involves additional cognitive processes, such as multitasking, attention, and conflict monitoring. In addition, it is challenging to(More)
This work examines support vector machine (SVM) classification of complex fMRI data, both in the image domain and in the acquired k-space data. We achieve high classification accuracy using the magnitude data in both domains. Additionally, we maintain high classification accuracy even when using only partial k-space data. Thus we demonstrate the feasibility(More)
This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess(More)
Radiofrequency (RF) encoding using spatially variant RF transmission fields represents an alternative to the conventional signal-encoding techniques applied in MRI, which are based on main field gradients. Thus, RF encoding might allow omitting the use of all main field gradients, alleviating acoustic noise and other main field gradient-related problems.(More)
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