Faculty Profiles
- brain machine interfaces
- computational neuroscience
- neural engineering
- statistical signal processing
Statistical signal processing, neural integration and coordination in sensorimotor systems, neural mechanisms of plasticity and learning, computational neuroscience. Applications in neuroinformatics, neuroprosthetics and brain-machine interfaces. His lab is interested in investigating how the activity of ensembles of neurons in the brain is integrated to give rise to an observed behavior. To fulfill this mission, his group is working on engineering advanced tools to simultaneously monitor the activity of single brain cells while subjects interact naturally with their surroundings. These tools range from miniaturized implantable microsystems that measure this microscale brain activity to sophisticated algorithms that process the activity to identify critical neural circuits governing multiple functions we perform in our daily life. The outcome of his research may help many people who suffer from multiple neurological diseases and disorders such as Parkinson's disease and Epilepsy.
- K. Oweiss, A. Mason, Y. Suhail, A. Kamboh, K. Thomson "A Scalable Wavelet Transform VLSI Architecture for Real-Time Signal Processing in High Density Intra-cortical Implants," IEEE Transactions on Circuits and Systems, Vol. 54, No. 6, June 2007
- K. Oweiss, "A Systems Approach for data compression and latency reduction in cortically-controlled brain machine interfaces," IEEE Transactions on Biomedical Engineering, vol. 53, no.7, pp. 1364 – 1377, Jul 2006
- S. Eldawlatly, R. Jin, and K. Oweiss, "Identifying Functional Connectivity in Large Scale Neural Ensemble Recordings: A Multiscale Data Mining Approach," Journal of Neural Computation, MIT press, 20: 1-28, 2008
- S. El-Dawlatly, K. Oweiss, "Identifying spike-timing dependent plasticity in spike train models of synaptically-coupled neuronal ensembles," BMC Neuroscience 2007, 8(Suppl 2): P193doi:10.1186/1471-2202-8-S2-P193