Abstract: In this talk, I will present some of our recent studies from the data-driven neuroscience of meditation (DANSOM) Lab. Our studies have helped quantify how meditation techniques impact human brain and health. The studies were conducted on new meditators over six weeks of practice with multiple meditation techniques including breath-focus and mantra-based meditation. Results showed improvements in various subjective stress and psychological measures and latencies in objective P300 speller tests (tied to cognitive performance) for meditating groups relative to a control group. Studies of neural dynamics for mantra-based meditation using Electroencephalography (EEG) revealed marked differences in alpha oscillatory dynamics for different mantras used during training. Complex mantras such as Hare Krishna may generate higher cognitive demands and induce more activating, attentionally focused meditative states with oscillatory patterns persisting after meditation, whereas simple mantras and breath-focus meditation promote more relaxed states. We also show the capability of machine learned models to distinguish cognitive/meditative states as well as different types of meditation techniques over time across many subjects, which can enable future neurofeedback applications for improving mental health. Along with presenting results, we will also have a hands-on experience of the studied meditation techniques.

Bio:  Saiprasad Ravishankar is currently an Associate Professor in the Departments of Computational Mathematics, Science and Engineering and Biomedical Engineering at Michigan State University (MSU). He directs the signals, learning, and imaging (SLIM) research group and the data-driven neuroscience of meditation (DANSOM) lab at MSU. He received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology Madras, India, in 2008, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering in 2010 and 2014, respectively, from the University of Illinois at Urbana-Champaign, where he was then an Adjunct Lecturer and a Postdoctoral Research Associate. From August 2015 to 2018, he was a postdoc in the Department of Electrical Engineering and Computer Science at the University of Michigan, and then a Postdoc Research Associate in the Theoretical Division at Los Alamos National Laboratory from August 2018 to February 2019, before joining MSU. His research interests include machine learning, computational and biomedical imaging, signal processing, image processing, inverse problems, data science, neuroscience, and physics and astrophysics applications. His research has been recognized with numerous awards including an NSF CAREER Award, IEEE Signal Processing Society Young Author Best Paper Award, and best student paper awards or finalist at numerous conferences such as the IEEE International Symposium on Biomedical Imaging (ISBI) 2018, IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2017, ISBI 2020, and Optical Imaging Congress 2023.

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