Fei He

I am an Assistant Professor (Senior Lecturer) in Centre for Computational Science and Mathematical Modelling, Coventry University, UK. My current research interests are at the interface between control systems engineering, signal processing and neuroscience - especially the use of nonlinear system identification to study complex nonlinear interactions in human brain network and diagnosis of neurological disorders (e.g. Alzheimer’s disease, tremor, seizures). I am also interested in statistical machine learnig, network inference and their applications in neuroscience and systems biology, e.g. identifying complex regulatory mechanisms in cellular (metabolic, genetic) networks.

I previously held research positions at Imperial College London (Theoretical Systems Biology group), University of Sheffield, and University of Manchester (Manchester Centre for Integrative Systems Biology). I received a PhD and an MSc (distinction) in control engineering from University of Manchester. I am the Associate Editor of IET Healthcare Technology Letters, editorial board member of Frontiers in Computational Neuroscience/Neuroinformatics, and have served as reviewer for a number of peer-reviewed journals (including 4 IEEE Transactions, Proc. IEEE, Automatica, Biophys. J., Cereb. Cortex, IET Systems Biology, Int. J. Control, Entropy). I am also the reviewer for international funding bodies (including EPSRC, Royal Society). Currently, I am a member of the UKRI Future Leaders Fellowships (FLF) peer review college, and a technical communitie member of Biomedical Signal Processing, IEEE Engineering in Medicine and Biology Society.

If you are interested to apply for a PhD studentship, a postdoc Fellowship (e.g. Marie Sklodowska-Curie, UKRI, Leverhulme, Newton International), or a visiting position in my group, please do not hesitate to get in touch with me.

E-mail: fei.he@coventry.ac.uk

Research interests

  • Nonlinear system identification: NARMAX modelling & frequency-domain analysis for EEG
  • Nonlinear connectivity, cross-frequncy coupling & causality analysis in neuroscience
  • Network inference for complex biochemical and neurological networks
  • Bayesian inference (parameter estimation and model selection), Gaussian Process, model-based experimental design

You can find my recent list of publications, ResearchGate, and University’s webpage here.

News!

  1. [September 2022] I have a funded visiting scholarship (6-12 months) for Early Career Researcher (e.g. Lecturer, Associate professor) from Universities in Jiang Su, China to visit my group. If you are interested, please get in touch with me before 18th Oct 2022.

  2. [September 2022] Glad to co-edit a Research Topic on Emerging Talents in Neuroinformatics: 2023 in Frontiers in Neuroinformatics. Welcome for submissions (especially student authors) - abstract deadline 08 Jan 2023.

  3. [September 2022] Our recent paper EEG-based Graph Neural Network Classification of Alzheimer’s Disease: An Empirical Evaluation of Functional Connectivity Methods, has been published in IEEE Transactions on Neural Systems and Rehabilitation Engineering.

  4. [July 2022] I was awarded an EPSRC grant (as Co-I) jointly with Prof. Eun-Jin Kim (PI) on ‘Information geometric theory of neural information processing and disorder’.

  5. [July 2022] I attended the 44th IEEE Engineering in Medicine and Biology Conference (EMBC) with my students Dominik Klepl and Stephan Goerttler, at Glasgow. Dominik presented his paper Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer’s disease.

  6. [March 2022] I gave an invited talk on “System Identification and Frequency-Domain Analysis in Neuroscience” at MRC Biomedical Engineering Workshop: Application of Engineering to Healthcare, at the University of Warwick, 3 March 2022.

  7. [March 2022] Our paper Characterising Alzheimer’s Disease with EEG-based Energy Landscape Analysis is now published in IEEE Journal of Biomedical and Health Informatics. It is also selected as the front cover featured article of the current issue. The results show AD patients’ brain network/state transitions are more constrained with more local minima, less variation and smaller basins. These features of Energy Landscape can potentially be used to predict AD with high accuracy.

  8. [Jan 2022] I am co-editing a Speical Issue on EEG Signal Processing Techniques and Applications in Sensors.

  9. [Dec 2021] I am co-editing a Special Issue for Frontiers in Computational Neuroscience & Frontiers in Neuroinformatics on Nonlinear Connectivity, Causality and Information Processing in Neuroscience. Welcome for submissions! Please submit your manuscript by 31 January, 2022.

  10. [Oct 2021] I organised a CSM spotlight series event on Nonlinear connectivity and frequency-domain analysis in neuroscience.

  11. [August 2021] New preprint - D Klepl, F He, M Wu, DJ Blackburn, P Sarrigiannis, Bispectrum-based Cross-frequency Functional Connectivity: A Study of Alzheimer’s Disease, bioRxiv, doi: https://doi.org/10.1101/2021.08.07.455499, August 2021.

  12. Our review on Nonlinear System Identification of Neural Systems from Neurophysiological Signals is published in Neuroscience. You can find the preprint here.