The Guarantors of Brain Postdoctoral Research Fellow
Shenghong He graduated with a D.Phil. in Engineering from South China University of Technology, China, in December 2017. His graduate work has mainly focused on developing real-time asynchronous brain-machine interface (BMI) methods and their applications using bioelectric signals including electroencephalogram (EEG) and electrooculography (EOG). In May 2018, Shenghong joined Dr Huiling Tan’s Group as a Postdoctoral Research Scientist in Neuroscience. Shenghong’s current research is focused on developing BMI systems based on subcortical local field potentials (LFPs) recorded from people with Parkinson’s disease, and testing the efficacy of this new BMI system in neuroprosthetic control and neurofeedback training. His work involves collecting behavioural and electrophysiological data, analysing brain signals in real-time to decode relevant information, using this information to drive an external actuator, and, at the same time, investigating the neural basis of the learning evident in BMI use in humans.
Balance between competing spectral states in Subthalamic nucleus is linked to motor impairment in Parkinson’s Disease
Khawaldeh S. et al, (2021), Brain: a journal of neurology
Subthalamic beta-targeted neurofeedback speeds up movement initiation but increases tremor in Parkinsonian patients
He S. et al, (2020), eLife, 9
Entraining stepping movements of Parkinson’s patients to alternating subthalamic nucleus deep brain stimulation
Fischer P. et al, (2020), The Journal of Neuroscience
A Hybrid Asynchronous Brain-Computer Interface Combining SSVEP and EOG Signals.
Zhou Y. et al, (2020), IEEE Trans Biomed Eng, 67, 2881 - 2892
Closed-loop DBS triggered by real-time movement and tremor decoding based on thalamic LFPs for essential tremor
He S. et al, (2020), Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 3602 - 3605