Summary
Song Luan received the M.Sc. in analogue and digital integrated circuit design and Ph.D. degrees in biomedical microelectronics engineering from Imperial College London in 2010 and 2014 respectively.In 2014, he works as a research associate in the Next Generation Neural Interfaces Lab.
He has designed different types of integrated neural stimulation circuits and systems with 0.35 and 0.18 um process. He is also an advanced engineer in PCB, firmware and software development for in-house custom hardware.
His main research interests include chronic implantable neural interfaces and its applications, low power microelectronics and wireless power/data link.
Publications
Conference
Luan S, Liu Y, Williams I, et al., 2016, An Event-Driven SoC for Neural Recording, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE, Pages:404-407, ISSN:2163-4025
Luan S, Williams I, de Carvalho F, et al., 2016, Next Generation Neural Interfaces for low-power multichannel spike sorting, FENS Forum of Neuroscience, FENS
Williams I, Rapeaux A, Liu Y, et al., 2016, A 32-Ch. Bidirectional Neural/EMG Interface with on-Chip Spike Detection for Sensorimotor Feedback, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE, Pages:528-531, ISSN:2163-4025
Williams I, Luan S, Jackson A, et al., 2015, A Scalable 32 Channel Neural Recording and Real-time FPGA Based Spike Sorting System, 11th IEEE Annual Biomedical Circuits and Systems Conference (BioCAS), IEEE, Pages:188-191, ISSN:2163-4025
Williams I, Luan S, Jackson A, et al., 2015, Live Demonstration: A Scalable 32-Channel Neural Recording and Real-time FPGA Based Spike Sorting System, 11th IEEE Annual Biomedical Circuits and Systems Conference (BioCAS), IEEE, Pages:187-187, ISSN:2163-4025