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Neural Systems Engineering & Information Processing Lab
Maryam M. Shanechi
Assistant Professor & Viterbi Early Career Chair
Ming Hsieh Department of Electrical Engineering – Systems
Viterbi School of Engineering
University of Southern California Office: EEB408



05/17/19 Our Journal of Neural Engineering paper on estimating functional connectivity in spike-field networks is now published online here.

05/16/2019 Our Journal of Neural Engineering paper on dynamic network modeling for human ECoG activity is now published online here.

04/23/2019 Maryam Shanechi receives the USC Viterbi Junior Research Award.

03/29/2019 Our IEEE Transactions on Neural Systems and Rehabilitation Engineering paper on how to compute multiscale causality graphs in spike-field networks is now published here.

03/18/2019 The joint meeting for two international MURI and BARI initiatives led by Maryam Shanechi is highlighted here.

03/11/2019 Our Journal of Neural Engineering paper on a personalized closed-loop brain interface system for anesthetic delivery is now published online here.

02/15/2019 We held a joint meeting for our MURI and BARI programs at USC with over 60 international experts from academia and government labs in US and UK. Led by Maryam Shanechi, MURI started in 2016 to develop brain-machine interfaces (BMIs) for enhanced decision accuracy and BARI was just kicked off to develop human-AI teams for joint decision making, funded by US DoD and UK MoD. Read more here.

02/11/2019 Science News has featured two of our papers published in Nature Biotechnology and Journal of Neural Engineering in an article about recent advances toward electrical stimulation treatments for depression here.

12/19/2018 Maryam Shanechi receives the ONR Young Investigator Award announced here.

10/24/2018 Our paper on multiscale decoding of spike-field activity is now published in Journal of Neural Engineering here.

10/11/2018 Our mood decoding paper is the cover article for the October issue of Nature Biotechnology here.

09/25/2018 DoD announces our new joint US-UK BARI program to build human-machine teams here.

09/17/2018 Our Journal of Neural Engineering paper on a computational framework for modeling the brain response to electrical stimulation is now published here.

09/11/2018 Read the “Behind the paper” story in Nature Communities about our new Nature Biotechnology mood decoding paper here.

09/10/2018 Our Nature Biotechnology paper on neural decoding of mood is now published here and the USC News Story is here. See also excerpts from Select Media Coverage: New Scientist, The Wall Street Journal, New Atlas

09/06/2018 We won the multi-institutional US-UK Bilateral Academic Research Initiative (BARI) award to work at the interface of AI and brain-machine interfaces.

06/29/2018 Watch Maryam’s invited lecture at the NAS Kavli Frontiers of Science on Brain-Machine Interfaces here

06/27/2018  Our research is featured in the IEEE’s The Institute publication here.

06/23/2018 Maryam Shanechi is elected to the grade of IEEE senior member.

06/15/2018  IEEE Brain Initiative 2-part podcast series with Maryam Shanechi discussing brain-machine interfaces is now available here.

Our PLoS Computational Biology paper on optimizing the learning rate in adaptive estimation of neural encoding models and decoders is now published here.

Maryam will chair a session at the National Academy of Sciences (NAS) 2018 Kavli Frontiers of Science Symposium on “Brain-Machine Interfaces”. Read more here.

Maryam will co-chair a session at the National Academy of Engineering (NAE) 2017 Frontiers of Engineering conference on “Unraveling the complexity of the brain”. Read more here.

Our lab receives a DURIP award from ARO to develop brain-machine interfaces.

Our new Nature Communications paper develops a brain-machine interface that significantly outperforms the existing state-of-the-art in neuroprosthetics. The work is featured in USC Viterbi news here.

Our paper on brain-machine interface control algorithms is now published online in IEEE Transactions on Neural Systems and Rehabilitation Engineering here.

Our Journal of Neural Engineering paper on a generalizable closed-loop system for control of anesthesia is now published here.

We held our joint US/UK MURI kick-off meeting at USC with participation from academia, DoD labs, and UK MoD. Our program is now officially launched. See the page for our MURI for more information.

My lab is awarded a joint US/UK multidisciplinary MURI grant to lead a multi-institutional collaboration that aims to build brain-machine interfaces for enhanced decision accuracy. Read more: USC news here , Viterbi news here, Press release here.

Our PLoS Computational Biology paper on robust brain-machine interface design is now available here.

Maryam Shanechi is recognized as one of the Popular Science Brilliant 10. Read more here.

Our paper first authored by Yuxiao Yang in the IEEE EMBC is selected as the geographic finalist from North America, and is announced as a winner of the best student paper competition. Congratulations Yuxiao! Read more here.

Maryam Shanechi is selected by the National Academy of Engineering (NAE) for the 2015 US Frontiers of Engineering (FOE) symposium. Read more here.

NSEIP lab recieves an inaugural Cal-BRAIN Award as part of California’s new grant program to develop neurotechnologies that revolutionize the understanding of the brain. Read more here.

Maryam Shanechi is appointed as the inaugural holder of a Viterbi Early Career Chair.

Maryam Shanechi receives the NSF CAREER Award. Read the USC news story here.

Our research has been selected by Google Solve for X as a “Tech Moonshot“. You can see our moonshot page and video here.

Watch a 3 minute video summarizing our research at the MIT Technology Review EmTech conference here.

Maryam Shanechi is selected as a Pioneer in the MIT Technology Review Innovators Under 35 list for her work on brain-machine interfaces. See the MIT Technology Review story here and the USC story here.

Our paper on a brain-machine interface for control of medical coma wins the best paper award in Technology, Computing, and Simulation at the 2014 International Anesthesia Research Society (IARS) annual meeting.

Our Nature Communication paper on a cortical-spinal prosthesis is now online and has been highlighted in various media outlets including Cornell Chronicle, BBC news, and Le Monde.

Our PLoS Computational Biology paper on a brain-machine interface for automatic control of anesthesia is now online and has been highlighted in various media outlets including Cornell Chronicle, MIT news, and NBC news.

Our PLoS ONE paper on a brain-machine interface to jointly decode the target and trajectory of movement using an optimal feedback control design is now online.

Our Nature Neuroscience paper on a brain-machine interface for enhanced sequential motor function is now online and has been highlighted in various outlets including Nature, MIT EECS News, and Discovery News.

Our IEEE TNSRE paper on a decoder for goal-directed movements from neural signals is now online.


Our laboratory works at the interface of statistical inference and signal processing, machine learning, and control 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. Some problems of interest include dynamic modeling of high-dimensional multiscale brain networks, decoding of cognitive or motor states from neural signals, developing closed-loop brain-machine interface (BMI) architectures for various applications, and devising closed-loop algorithms for control of neural signals using stimulation.

Some applications of interest include developing BMIs that aim to restore motor function in disabled patients. These BMIs record the neural activity in the relevant brain areas and use diverse mathematical tools to infer from this activity the motor intent of the user. We also develop BMIs for automatic closed-loop control of the brain state under anesthesia that adjust the real-time anesthetic infusion rate based on non-invasive neural recordings. Finally, we design BMIs for treatment of neuropsychiatric disorders that decode cognitive states and perform closed-loop brain stimulation.


Postdoctoral Position Available!
We’re recruiting postdoctoral scholars with signal processing, statistical inference, or control background to develop algorithms for neural systems. The project involves both theoretical development and algorithm design, and validation with brain data. Interested candidates should apply by sending their CV to

Graduate Student Openings!

We’re recruiting PhD students with strong mathematical background who are interested in developing signal processing and control-theoretic algorithms for neural engineering applications. Interested candidates should contact