Imperial College London

Nur Ahmadi

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Postgraduate







B422Bessemer BuildingSouth Kensington Campus






Nur Ahmadi received the B.Eng. degree in electrical engineering from Bandung Institute of Technology (ITB), Indonesia, in 2011. Subsequently, he was awarded Monbusho/MEXT scholarship for pursuing M.Eng degree at the Department of Communication and Integrated Systems, Tokyo Institute of Technology, Japan. After graduated in 2013, he moved back to Indonesia and worked at the Microelectronics Centre, ITB for 2.5 years. In 2016, he joined the Next Generation Neural Interfaces (NGNI) Lab, within the Department of Electrical & Electronic Engineering and Centre for Bio-Inspired TechnologyImperial College London to pursue a Ph.D. degree funded by LPDP scholarship.

phd thesis topic

Neural Signal Processing and Decoding Methods for Intracortical Brain-Machine Interfaces

research interests

Digital signal processing, biomedical signal processing, artificial intelligence, machine learning, deep learning, digital IC/VLSI design, embedded system, system on chip, brain-machine interface, healthcare and neurotechnology



Ahmadi N, Constandinou T, Bouganis C, 2018, Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS), Plos One, Vol:13, ISSN:1932-6203


Ahmadi N, Bouganis C, Constandinou T, End-to-end hand kinematics decoding from local field potentials using temporal convolutional network, IEEE Biomedical Circuits and Systems (BioCAS) Conference, IEEE

Ahmadi N, Cavuto ML, Feng P, et al., 2019, Towards a distributed, chronically-implantable neural interface, 9th IEEE/EMBS International Conference on Neural Engineering (NER), IEEE, Pages:719-724, ISSN:1948-3546

Ahmadi N, Constandinou TG, Bouganis C-S, 2019, Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER 2019), Pages:1-5

Ahmadi N, Constandinou TG, Bouganis C, 2018, Spike rate estimation using Bayesian Adaptive Kernel Smoother (BAKS) and its application to brain machine interfaces, 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE

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