Anirudh Wodeyar

Anirudh Wodeyar

Assistant Professor

Department of Advanced Computing Sciences · Maastricht University

Statistics  ·  Signal Processing  ·  Neuroscience

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About

I am an Assistant Professor in the Department of Advanced Computing Sciences at Maastricht University. I bring together a background in computer science, statistics, and signal processing to develop methods that help us better understand the brain — and potentially move brain dynamics toward healthier states.

I enjoy thinking about oscillations: in particular, about how they are generated and what characterizes them in the brain. Much of my work develops statistical and signal-processing tools to track, model, and analyze neural rhythms, with applications across epilepsy, sleep, and stroke recovery.

Research

My research sits at the intersection of systems neuroscience and real-time signal processing, with recurring themes of oscillations, networks, epilepsy and sleep. A few directions I am currently excited about:

The difficulty of estimating networks from time-series

Estimating networks is critical across a range of fields but especially in neuroscience. Our current work looks at how we can estimate networks from point-processes such as those that represent epileptic spike events.

State-space modeling for non-sinusoidal and bursty rhythms

By modeling oscillations as damped harmonic oscillators with time-varying frequencies driven by noise, a state-space framework lets us track — in real time — rhythms that are poorly represented as simple band-limited oscillations.

Constraints from sleep oscillations

Sleep rhythms have been implicated in overnight memory consolidation. How can we quantify this in a way that gives explanatory power to the rhythms themselves, rather than only to the neuronal spikes that support them?

Publications

A selection of my work is below. For the complete and most up-to-date list, see my Google Scholar profile. (Author names in bold indicate my contribution; titles link to the freely available version.)

  1. Conference Real-time sub-cycle oscillatory beta burst detection. Wodeyar A, Karel J, Peeters R. IEEE EMBC 2026.
  2. Preprint A hierarchical cascade of sleep rhythms drives memory consolidation in humans and is disrupted in epilepsy. Wodeyar A, Chinappen D, Kwon H, Shi W, Richardson M, Kramer MA, Chu CJ bioRxiv, 2025.
  3. Thalamic engagement by epileptic spikes as a mechanism for widespread slow oscillation–spindle decoupling. Wodeyar A, Kramer MA, Chu CJ. Epilepsia, 66(7):2600, 2025.
  4. Auditory-evoked changes in slow oscillations and spindles correlate with memory consolidation in children with epilepsy and controls. Kwon H, Chinappen DM, Kinard EA, Goodman SK, ... Wodeyar A..., Chu CJ Clinical Neurophysiology, 2025.
  5. Preprint Structural and EEG motor networks distinguish level of motor impairment after stroke. Wodeyar A, Zhou Z, Cramer SC, Srinivasan R. medRxiv, 2025.
  6. Conference Real-time navigational intent detection from hippocampal EEG: a proof of concept. Savvides N, Bonizzi P, Herff C, Wodeyar A. 37th Benelux Conference on Artificial Intelligence (BNAIC), 2025.
  7. Thalamic epileptic spikes disrupt sleep spindles in patients with epileptic encephalopathy. Wodeyar A, Chinappen D, Mylonas D, Baxter B, Manoach DS, Eden UT, Kramer MA, Chu CJ Brain, 147(8):2803–2816, 2024.
  8. Different methods to estimate the phase of neural rhythms agree but only during times of low uncertainty. Wodeyar A, Marshall FA, Chu CJ, Eden UT, Kramer MA. eNeuro, 10(11), 2023.
  9. Structural connectome constrained graphical lasso for MEG partial coherence. Wodeyar A, Srinivasan R. Network Neuroscience, 6(4):1219–1242, 2022.
  10. Sensory stimulation-based protection from impending stroke following MCA occlusion is correlated with desynchronization of widespread spontaneous local field potentials. Rasheed W, Wodeyar A, Srinivasan R, Frostig RD. Scientific Reports, 12(1):1744, 2022.
  11. A state-space modeling approach to real-time phase estimation. Wodeyar A, Schatza M, Widge AS, Eden UT, Kramer MA. eLife, 10:e68803, 2021.
  12. Coherent neural oscillations inform early stroke motor recovery. Cassidy JM, Wodeyar A, Srinivasan R, Cramer SC. Human Brain Mapping, 42(17):5636–5647, 2021.
  13. Low-frequency oscillations are a biomarker of injury and recovery after stroke. Cassidy JM, Wodeyar A, Wu J, Kaur K, Masuda AK, … Srinivasan R, Cramer SC. Stroke, 51(5):1442–1450, 2020.
  14. Damage to the structural connectome reflected in resting-state fMRI functional connectivity. Wodeyar A, Cassidy JM, Cramer SC, Srinivasan R. Network Neuroscience, 4(4):1197–1218, 2020.
  15. Rapid development of strong, persistent, spatiotemporally extensive cortical synchrony and underlying oscillations following acute MCA focal ischemia. Wann EG, Wodeyar A, Srinivasan R, Frostig RD. Scientific Reports, 10(1):21441, 2020.
  16. Preprint Network structure during encoding predicts working memory performance. Wodeyar A, Srinivasan R. bioRxiv, 2018.

Contact

I am always happy to talk about oscillations, real-time methods, and potential collaborations or student projects.