Rosa So

Dr. Rosa SO Qi Yue

Adjunct Assistant Professor

Division Head, Healthcare and Medtech Division, Institute for Infocomm Research

Appointment

  • Adjunct Assistant Professor, Biomedical Engineering Department, NUS
  • Division Head, Healthcare and Medtech Division, Institute for Infocomm Research, A*STAR
Email: rosa-so@i2r.a-star.edu.sg (primary email),  rosa-so@nus.edu.sg

Academic qualifications

  • Duke University, Ph.D, Biomedical Engineering (2012).
  • Johns Hopkins University, B.Sc., Biomedical Engineering (2006).

Research Interest

  • Brain-machine interface
  • Brain stimulation
  • Neural interfaces

Selected Awards & Honours

  • A*STAR National Science Scholarship BS-PhD (2003 – 2012)

Membership in Scientific/Professional Organization

  • IEEE – Member
  • IEEE Engineering in Medicine and Biology Society (EMBS) Singapore chapter –Executive committee member (Treasurer)

Selected Journal Publications

  • Liao JJ, Luo JJ, Yang T, So RQ, Chua MC. (2020) Effects of Local and Global Spatial Patterns in EEG Motor-Imagery Classification using Convolutional Neural Network. Brain Computer Interfaces (preprint)
  • Shaikh S, So RQ, Sibindi T, Libendinsky C, Basu A. (2020) Sparse Ensemble Machine Learning to Improve Robustness of Long-Term Decoding in iBMIs. IEEE Trans Neural Syst Rehabil Eng. 28(2):380-389.
  • Shrivastwa RR, Pudi C, Duo C, So RQ, Chattopadhyay A and Guan C. (2020) A Brain–Computer Interface Framework Based on Compressive Sensing and Deep Learning. IEEE Consumer Electronics Magazine. 9 (3): 90-96.
  • Zhang X, Libedinsky C, So RQ, Principe JC, WangY. (2019) Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces. IEEE Trans Neural Syst Rehabil Eng 27(9):1684-1694.
  • Shaikh S, So R, Sibindi T, Libendinsky C, Basu A. (2019) Towards Intelligent Intracortical BMI (i2BMI): Low-Power Neuromorphic Decoders that Outperform Kalman Filters. IEEE Trans Biomed Circuits Syst 13(6):1615-1624
  • So RQ, McConnell GC, Grill WM. (2017) Frequency-dependent, transient effects of subthalamic nucleus deep brain stimulation on methamphetamine-induced circling and neuronal activity in the hemiparkinsonian rat. Behavioral Brain Research 320:119-127.
  • So RQ*, Krishna V*, King NKK, Yang HJ, Zhang Z, Sammartino F, Lozano AM, Wennberg RA, Guan CT. (2017) Prediction and detection of seizures from simultaneous thalamic and scalp EEG recordings. J Neurosurgery 126(6):2036-2044.
  • Brocker DT, Swan BD, So RQ, Turner DA, Gross RE, Grill WM. (2017) Optimized temporal pattern of brain stimulation designed by computational evolution. Science Translation Medicine. 9(371).
  • Libedinsky C*, So RQ*, Xu ZM, Toe KK, Ho D, Lim C, Chan L, Chua YW, Lei Y, Cheong JH, Lee JH, Vishal KY, Guo YX, Chen ZN, Lim LK, Li P, Liu L, Zou X, Ang KK, Gao Y, Ng WH, Han BS, Chng K, Guan CT, Je MK, Yen SC. (2016) Independent Mobility Achieved Through a Wireless Brain-Machine Interface. Plos ONE 1;11(11):e0165773 .
  • McConnell GC*, So RQ*, Grill WM. (2016) Failure to suppress low-frequency neuronal oscillatory activity underlies the reduced effectiveness of random patterns of deep brain stimulation. J Neurophysiology 115(6):2791-802.
  • Krishna V, Sammartino F, King NKK, So RQ, Wennberg RA (2016) Neuromodulation for Epilepsy. Neurosurgery Clinics of North America. 27(1):123-31.
  • McConnell GC*, So RQ*, Hilliard JD, Lopomo P, Grill WM (2012) Effective deep brain stimulation suppresses low frequency network oscillations in the basal ganglia by regularizing neural firing patterns. Journal of Neuroscience 32(45):15657-68.
  • So RQ, McConnell GC, August AT, Grill WM (2012) Characterizing effects of subthalamic nucleus deep brain stimulation on methamphetamine-induced circling behavior. IEEE Transactions on Neural Systems and Rehabilitation Engineering 20(5):626-635
  • So RQ, Kent AR, Grill WM (2011) Relative contributions of local cells and passing fiber activation and silencing to thalamic fidelity during DBS and lesioning: a computational modeling study. Journal of Computational Neurosciece 32(3):499-519