Ehsan Eqlimi

Ehsan Eqlimi currently works at WAVES research groupGhent University, Belgium. His work mainly focuses on developing the signal processing algorithms for brain functions and networks, through EEG, fNIRS, and fMRI. He also has interests in array signal processing including sparse representation,  blind source separation, and tensor factorization. 

Currently, he investigates how can the auditory attention/perception to speech in noise be monitored by single-trial EEG signal processing.

 He received the B.Sc. and M.Sc. degrees in Bioelectrical Engineering from Sahand University of Technology  and Tehran University of Medical Sciences, Iran in 2010 and 2013, respectively.

His Master thesis was about "Analysis of Brain Networks in functional MRI using graph theory for patients with MS " (see here).

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

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

  • Auditory attention to speech in noise using EEG

[A1] Eqlimi, E., Bockstael, A., De Coensel, B., Schönwiesner, M., Talsma, D., & Botteldooren, D. (2020). EEG correlates of learning from speech presented in environmental noise (submitted).

[C1] E.Eqlimi, A. Bockstael, B. e Coensel, D. Botteldooren, Evaluating potential EEG-indicators for auditory attention to speech in realistic environmental noise, in proceeding of  23rd International Congress on Acoustics, Aachen, Germany (ICA 2019) , pp. 7631-7638.

[C3] E.Eqlimi,  D. Botteldooren, A. Bockstael, Brain Monitoring of distraction from speech in noisy context using EEG, in Speech in Noise Workshop (SpiN 2019), Ghent, Belgium. 

  • Sparse component analysis

[A1]  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. (Matlab Codes)

[A1] E. Eqlimi, B. Makkiabadi, A.Fotouhi, S.Sanei. (2020) Underdetermined Blind Identification for k-Sparse Component Analysis
using RANSAC-based Subspace Search (submitted).

[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 (Matlab Codes)

[C1]  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 (Matlab Code)

Matlab Codes: Ehsan Eqlimi, & Bahador Makkiabadi. (2020, April 14). Sparse-USR-EigMem v1.1 (Version v1.1). Zenodo. http://doi.org/10.5281/zenodo.3752005

  •  EEG source reconstruction based on tensor factorization

[C1] 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.

[C1] 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.

  • fMRI data analysis

[C1]  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 theoryXIII Mediterranean Conference on Medical and Biological Engineering and Computing, 2013 (41) (2013), pp. 206-209

 

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