Ehsan Eqlimi

Ehsan Eqlimi currently works at WAVES research group, Department of Information Technology (INTEC), Ghent University (UGent) /IMEC company, Ghent, Belgium. His work mainly focuses on developing the signal processing algorithms for brain functions and networks, through EEG, MEG, and fMRI. He also has interests in sparse representation and compressed sensing.

 He received the BSc and MSc degrees in Biomedical Engineering from Sahand University of Technology (SUT) and Tehran University of Medical Sciences (TUMS), Iran in 2010 and 2013, respectively. His Master thesis was "resting-state fMRI connectivity analysis for patients with Multiple Sclerosis (MS) based on graph theory".

 He has been a senior researcher since 2013 for 4 years at Tehran University of Medical Sciences (TUMS). During this time, he focused on EEG source localization for motor intention decoding. He also has developed and published some novel algorithms on underdetermined blind identification and source recovery based on the sparse component analysis. 

He has the experience of working on "image processing and pattern recognition"  for the biometric application as an R&D engineer (2011-2016) in R&D departments of several reputable companies in Tehran, Iran. 

Currently, he investigates how can the auditory attention be monitored by single-trial EEG signal processing. 

- Publications in Sparse Component analysis:

[J1]  E. Eqlimi, B. Makkiabadi, N. Samadzadehaghdam, H. Khajehpour, F. Mohagheghian, S. Sanei, A novel underdetermined source recovery algorithm based on k-sparse component analysis, Circuits, Systems, and Signal Processing 38 (3) (2019) 1264-1286.

[C1]  E. Eqlimi, B. Makkiabadi, An efficient K-SCA based underdetermined channel identification algorithm for online applications, in 23rd European Signal Processing Conference (EUSIPCO) (IEEE, 2015), pp. 2661–2665

[C2]  E. Eqlimi, B. Makkiabadi, Multiple sparse component analysis based on subspace selective search algorithm, in 2015 23rd Iranian Conference on Electrical Engineering (ICEE)(IEEE, 2015), pp. 550–554

- Publications in Tensor Factorization for EEG source reconstruction:

[C4] A. Fotouhi, E. Eqlimi, B. Makkiabadi, Adaptive localization of moving EEG sources using augmented complex tensor factorization, in: 2017 40th International Conference on Telecommunications and Signal Processing (TSP), IEEE, 2017, pp. 439-443.

[C5] A. Fotouhi, E. Eqlimi, and B. Makkiabadi, “Evaluation of adaptive PARAFAC algorithms for tracking of simulated moving brain sources,” in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE, pp. 3819–3822, IEEE, 2015.

- Publication in fMRI data analysis:

[C6]  E. Eqlimi, N. Riyahi Alam, M. Sahraian, A. Eshaghi, S. Riyahi Alam, H.Ghanaati, K. Firouznia, E. Karami, Resting state functional connectivity analysis of multiple sclerosis and neuromyelitis optica using graph theory, XIII Mediterranean Conference on Medical and Biological Engineering and Computing, 2013 (41) (2013), pp. 206-209





EEG signal processing, EEG source reconstruction, sparse representation, compressed sensing, blind source separation, brain connectivity