SPS BISP TC Webinar: 6 December 2022, presented by Dr. Borbála Hunyadi

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News and Resources for Members of the IEEE Signal Processing Society

December 2022

SPS BISP TC Webinar: 6 December 2022, presented by Dr. Borbála Hunyadi

Webinar.jpg

Education & Resources

Upcoming SPS-BISP Webinar

Title: Tensor Decompositions in Functional Neuroimaging
Date: 6 December 2022
Time: 3:00 PM (CET) 
Duration: Approximately 1 Hour
Presenters: Dr. Borbála Hunyadi, (TU Delft)

Register for the Webinar

Abstract:

Brain data are inherently large scale, multidimensional, and noisy. Indeed, advances in imaging and sensor technology allow recordings of ever-increasing spatio-temporal resolution. Multidimensional, as time series data are recorded at multiple locations (electrodes, voxels), from multiple subjects, under various conditions. Finally, the data are noisy: the recorded observations are a mixture of ongoing brain activity, physiological, and non-physiological noise sources. Tensors (higher order arrays) are the natural representations of such multidimensional data. Tensor decompositions, in general, aim to write a large and high-order tensor in terms of the product and summation of several smaller and low-rank tensors (including vectors and matrices). A tensor decomposition with a well-chosen number of terms and ranks can approximate the original data tensor using much fewer entries; even to capture the underlying sources separately in its individual components. This talk will first give an introduction to multilinear algebra and tensor decompositions, discuss current challenges in large-scale brain data analysis, and finally highlight some successful applications of tensor decompositions in EEG and functional ultrasound (fUS) data processing.

Biography:

Borbala Hunyadi

Dr. Borbála (Bori) Hunyadi was born in Budapest, Hungary. She received the M.Sc. degree in electrical and computer engineering from the Pazmany Peter Catholic University in 2009. In the same year, she joined Stadius, Department of Electrical Engineering at KU Leuven, where she worked in close collaboration with the Laboratory for Epilepsy Research, and received the Ph.D. degree in 2014.
 
She continued working in Stadius as a postdoctoral researcher on the ERC advanced grant Biotensors, and she served as the research lead on the Imec-ICON project SeizeIT. Between February and May 2016, she was a visiting researcher at the University of Oxford. In 2018, she was awarded one of the “Delft Technology Fellowships” for outstanding female academic researchers. In October 2018, she joined the Circuits and Systems group at TU Delft as an assistant professor.  Her research interests include biomedical signal processing and machine learning for biomedical pattern recognition. More specifically, she is interested in multimodal signal processing and fusion, blind source separation, tensor decompositions, and wearable signal processing to better understand healthy and pathological physiology. In particular, she is leading projects on ECG, EEG and (functional) ultrasound signal processing for a variety of applications including atrial fibrillation, epilepsy, neuroscience research and cancer detection.
 
Dr. Hunyadi is the secretary of the IEEE EMBC Benelux chapter and vice-chair of the EURASIP technical area committee on biomedical signal and image processing.

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