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 neurobiomarker discovery in neurological 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.
Development of Advanced Neural Decoding Algorithms for the 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 and Jiang et al., 2020]. We collaborate with several institutions in the US in this particular direction.
Neurobiomarker Discovery for the Optimization of DBS
The goal of this work is to explore the neural activity of brain to identify neural-biomarkers for the optimization of deep brain stimulation (DBS) in Parkinson Disease. We collaborate with Dr. Aviva Abosch, in this particular direction and obtain data from the subthalamic nucleus (STN) 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. We 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]. We also showed that β band and high frequency oscillations of STN-LFPs can be sued to predict the optimal track to implant the chronic DBS electrode [Telkes et al., 2016 and Ozturk et al., 2020].
Investigation of High Frequency Oscillations in Epilepsy using Computational Intelligence
This project aims at the investigation of neural-biomarkers in patients with refractory epilepsy, for the purpose of accurate seizure onset zone (SOZ) identification and assisting in surgical planning. Epilepsy is one of the most common neurological diseases. Surgical resection of SOZ can provide seizure freedom for many patients with drug resistant epilepsy, which requires invasive EEG (iEEG) monitoring over an extended period of time and detailed visual inspection of collected data by neurologists. High frequency oscillations (HFO, 80 – 500 Hz) are highly valued as a promising clinical biomarker for epilepsy. Due to the low amplitude and short duration of these events, visual identification of HFO transients in the clinical recordings of vast size is infeasible, which has limited the utilization of HFOs as SOZ indicators in clinical practice. In the current project, we mainly focus on the quantitative detection and characterization of HFOs in continuous human iEEG data using machine learning and other computational methods. We develop novel algorithms to effectively detect HFO and its subtypes in massive clinical iEEG datasets in an automated fashion, refine the detection by isolating HFOs from inter-ictal spikes and other non-neural events, and investigate the prognostic value of signature activities such as HFOs and interictal spikes Liu et al., 2016 and Liu et al., 2020].