Closed-Loop Multisensory Brain-Computer Interface for Enhanced Decision Accuracy
“Our laboratory develops neurotechnology and studies the brain through modeling, decoding and control of neural dynamics. To do so, we work at the interface of machine learning, statistical inference, signal processing, and control theory to develop algorithmic solutions for problems in basic and clinical neuroscience that involve the collection and manipulation of neural signals. Our work combines algorithm development and modeling with in vivo experimental implementation and testing, and is conducted in close collaboration with a variety of experimental labs. Our lab has developed brain-machine interfaces for decoding and closed-loop control of various brain states to restore lost emotional function in mental disorders and lost motor function in neurological injuries and diseases. Some specific problems of interest include dynamic modeling of high-dimensional multiscale brain network activity, decoding of cognitive, motor, or mood states from neural signals, and developing closed-loop neurotechnologies such as closed-loop deep brain stimulation systems and brain-machine interfaces. These closed-loop technologies have the potential to enable personalized therapies for a wide range of neurological and neuropsychiatric disorders.”
Song C., Hsieh, H., Pesaran B., Shanechi M. M., “Modeling and Inference Methods for Switching Regime-Dependent Dynamical Systems with Multiscale Neural Observations”, bioRxiv , Jun. 2022 (link ).
Wang C., Pesaran B., Shanechi M.M., “Modeling multiscale causal interactions between spiking and field potential signals during behavior”. Journal of Neural Engineering, Jan. 2022. (link)