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Yaser Fathi |
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People
Director Researchers Ph.D. Candidate Students Alumni
 Yaser Fathi received the B.S. degree in electrical engineering from Shahid Beheshti University, Tehran, Iran, in 2010, the M.S. degree in biomedical engineering from Tarbiat Modares University, Tehran, Iran, 2013, and the Ph.D. degree in biomedical engineering from Iran University of Science and Technology, Tehran, Iran, in 2021. The main focus of his research during recent years was to investigate the spinal cord neural activities for decoding hindlimb kinematic information during movement and its application to lower limb neuroprosthetic devices. Currently, his main goal is to reconnect the neural system after spinal cord injury by simultaneous recording neural activities from one side and stimulating the neural populations at the other side of the lesion to bypass the spinal cord injury and restore limb functions. His research interests include neural interfaces, spinal cord recording, neural networks, biomedical signal processing, and machine learning. He is particularly interested in developing robust learning algorithms, feature extraction, and decoding methods for long-term applications in neural interfaces.
Publications:
Y. Fathi and A. Erfanian, “Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion,” J. Neural Eng., vol. 18, no. 2, p. 026015, 2021.
Y. Fathi and A. Erfanian, “A Probabilistic Recurrent Neural Network for Decoding Hind Limb Kinematics from Multi-Segment Recordings of the Dorsal Horn Neurons” J. Neural Eng., vol. 16, no. 3, p. 036023, 2019.
H. Yeganegi, Y. Fathi, and A. Erfanian, “Decoding hind limb kinematics from neuronal activity of the dorsal horn neurons using multiple level learning algorithm,” Sci. Rep., vol. 8, no. 1, p. 577, 2018.
Y. Fathi, A. Mahloojifar, and B. Mohammadzadeh Asl "Real Time Implementation of Adaptive Beamformer in Medical Ultrasound Imaging Based on Parallel Processing with Graphical Processing Units" Journal of Acoustical Engineering Society of Iran, Vol. 1, No. 1, pp. 47-56, Oct. 2013.
Y. Fathi and A. Erfanian, “Stacked recurrent neural network for decoding of reaching movement using local field potentials and single-unit spikes,” in 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), 2017, pp. 672–675.
Y. Fathi, A. Mahloojifar, B. Mohammadzadeh Asl “GPU-Based Adaptive Beamformer for Medical Ultrasound Imaging”, 19th Iranian Conference on Biomedical Engineering, Amirkabir University of Technology, Tehran, 2012. |
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