The activities in my lab include a variety of basic and translational research in the rapidly growing area of neural engineering and biomedical signal processing. Areas of special interest are: neural decoding for neuroprosthetics; machine learning for neuromarker discovery in cognitive and movement disorders; development of embedded wearable wireless sensors and their integration to intelligent systems for healthcare and assisted living. In particular, we develop novel algorithms and machine learning techniques to explore neural activity recorded in clinical setting. My lab focuses on research that contributes not only to algorithm development but also to the discovery of new methods for diagnosis and therapy that can be applied in clinical practice. In this scheme, our group works closely with clinicians and researchers from diverse fields such as neuroscience, neurosurgery and neurology. For more information, please visit my lab website at http://incelab.bme.uh.edu/.
Development of Advanced Neural Decoding Algorithms for Next Generation Neuroprosthetics
The objective is to develop novel feature extraction and signal processing algorithms to decode the neural activity of brain for a neuroprostetics. In particular, we utilize adaptive signal processing and compressive sensing techniques to extract parsimonious features from high density neural recordings from non-human primates [Ince et al., PlosONE 2010] and as well as human subjects [Onaran et al., BSPC 2012]. We collaborate with several institutions in US and China in this particular direction.
Neuromarker Discovery for the Optimization of DBS
The goal of this work is to explore the neural activity of brain to identify neuro biological markers for the optimization of deep brain stimulation in Parkinson Disease. We collaborate with Dr. Aviva Abosch, in this particular direction and obtain data from the sub thalamic nucleous of Parkinsonian subject who are implanted DBS electrodes. Recently, with a funding provided by the Medtronic Inc., we investigated the relationship between local field potential (LFP) activities in the subthalamic nucleus and the therapeutic response to programming. e showed that postoperative subband analysis of LFP recordings in β and γ frequency ranges may be used to select optimal electrode contacts. These results indicate that LFP recordings from implanted DBS electrodes can provide important clues to guide the optimization of DBS therapy in individual patients [Ince et. al, Neurosurgery 2010].