Omid G. Sani

This study decodes continuous goal-directed movement from brain-wide intracranial recordings using PSID, showing widespread neural correlates across multiple brain regions.

This paper develops a method to dissociate intrinsic neural dynamics from input-driven dynamics in neural-behavioral data, showing it better captures behaviorally relevant dynamics across tasks and subjects.

This paper introduces a multiscale subspace identification algorithm for modeling multimodal spiking and field potential data, showing improved efficiency and accuracy in capturing low-dimensional dynamics and predicting behavior.

This paper investigates whether decision confidence can be decoded from EEG signals post-stimulus and pre-response to enable a brain-computer interface for improving decision making.

This paper introduces RNN PSID, a nonlinear dynamic modeling method for analyzing neural dynamics, and shows it outperforms linear and non-preferential methods in behavior decoding.

This paper introduces PSID, a method to dissociate behaviorally relevant neural dynamics from irrelevant ones, revealing lower-dimensional and more predictive dynamics in monkey motor tasks.