Dr Javier Escudero Rodriguez

Chancellor's Fellow

Email: 

Telephone: 

+44(0)131 6505599

Location: 

Alexander Graham Bell building

Social Media: 

Engineering Discipline: 

  • Electronics and Electrical Engineering

Research Institute: 

  • Digital Communications

Research Theme: 

  • Signal and Image Processing
Dr Javier Escudero Rodriguez
Dr Javier Escudero Rodriguez

Biography: 

I am a signal processing engineer who analyses biomedical data. My main aim is to reveal the subtle changes that major diseases (e.g., Alzheimer's and epilepsy) cause in the brain activity and how this changes in different conditions and mental states.

In October 2013, I joined the Institute for Digital Communications, School of Engineering, as a Chancellor's Fellow in biomedical signal processing.

Previously, I held a post-doctoral position at the School of Computing and Mathematics of Plymouth University.

My training includes an MEng in telecommunications engineering from the University of Valladolid (Spain) in 2005 and a PhD in biomedical signal processing from the same university in 2010.

Academic Qualifications: 

  • 2005 - MEng in Telecommunications Engineering (First) - University of Valladolid (Spain)
  • 2010 - PhD in Telecommunications Engineering - University of Valladolid (Spain)

Research Interests: 

Biosignal Processing

You can watch this video on Media Hopper or on YouTube.

In collaboration with researchers at Edinburgh, across the UK and overseas, I am currently working in the processing and analysis of biomedical signals, particularly human brain activity. By applying advanced mathematics, I aim at increasing our understanding of how several brain conditions progress. I have expertise in noise reduction and feature extraction for diverse biomedical recordings.

Of particular interest is the evaluation of brain functional connectivity in both neurodevelopmental and neurodegenerative diseases to understand how they affect the way in which different brain regions interact with each other.

I am also interested in the application of pattern recognition techniques to highly-dimensional clinical datasets to support decision making and in the development of non-invasive methods for rehabilitation purposes, being either the dexterous controls prostheses for amputees or brain-computer interfaces.

Specialities: 

  • Biomedical signal processing