Summary
Dr Deren Barsakcioglu is a research associate at Department of Bioengineering at Imperial College London. He received B.Sc. degree in Electrical and Computer Engineering from University of Texas at Austin, and M.Sc. in Analogue and Digital Integrated Circuit Design at Imperial College London in 2010 and 2011, respectively. He later joined Next Generation Neural Interfaces research group at Imperial College where he received his PhD in biomedical circuits and systems in 2016.
Following his PhD, Dr Barsakcioglu was awarded EPSRC Doctoral Prize Fellowship and worked as a Research Associate in Next Generation Neural Interfaces research group at the Department of Electrical and Electronic Engineering. In October 2017, he joined Farina Group at Department of Bioengineering where he currently carries out research on neural signal processing for spike sorting and electrophysiological decoding of human movements in upper limb prosthesis control.
Dr Barsakcioglu's main research interest is developing technologies for human-machine interfacing. His research focuses on biomedical signal processing, machine learning and real-time embedded systems.
Publications
Journals
Mendez Guerra I, Barsakcioglu DY, Vujaklija I, et al., 2022, Far-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors, Journal of Neural Engineering, Vol:19, ISSN:1741-2552
Eden J, Bräcklein M, Ibáñez J, et al., 2022, Principles of human movement augmentation and the challenges in making it a reality, Nature Communications, Vol:13, ISSN:2041-1723
Puttaraksa G, Muceli S, Barsakcioglu DY, et al., 2022, Online tracking of the phase difference between neural drives to antagonist muscle pairs in essential tremor patients, Ieee Transactions on Neural Systems and Rehabilitation Engineering, Vol:30, ISSN:1534-4320, Pages:709-718
Bräcklein M, Ibáñez J, Barsakcioglu DY, et al., 2021, The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal
Clarke AK, Atashzar SF, Vecchio AD, et al., 2021, Deep learning for robust decomposition of high-density surface EMG signals, Ieee Transactions on Biomedical Engineering, Vol:68, ISSN:0018-9294, Pages:526-534