Biomedical Data Analysis Research Laboratory (BDARL)
Active Research Works
Active Research Works
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Developing Deep Learning Algorithms to Diagnose Subtypes of Depression
This project, in collaboration with the Department of Psychiatry and Behavioral Neuroscience, McMaster University, explores a novel deep learning algorithm (DLA) based on effective connectivity between brain’s regions that are extracted from resting electroencephalography (EEG) data to diagnose different subtypes of depressions including major depressive disorder, depressive episode of bipolar disorder, manic episode of bipolar disorder, atypical disorder, and psychotic disorder as well as schizophrenia.
Developing Quantitative Sensing Technologies to Measure the Fine Motor Skills and Evaluate the Efficacy of Therapeutic Interventions for Autistic Children
In this study, in collaboration with the Department of Occupational Therapy, New York Tech, researchers are developing sensing and haptic technologies that can quantitatively measure and correct the patterns of the fine motor activities that can be used to 1) improve fine motor skills and 2) evaluate the efficacy of the outcomes of the therapeutic intervention. The inexpensive, non-invasive, and accessible tools, designed through this technology, can assist ASD children to improve their motor skills and help the clinician to better evaluate the efficacy of the intervention.
EEG Biomarkers to Investigate the Effect of Vagus Nerve Stimulation (VNS) on the Brain’s Functional Activity using Vagal-Evoked Potential
Vagus nerve stimulation (VNS) is a nonpharmacological treatment for epilepsy and depression and has been tested as a possible therapy for tinnitus, post-traumatic stress disorder, headache, sleep disorders, and neurorehabilitation after stroke. However, the cortical effects of VNS, manifesting as vagal-evoked potentials (VEPs) at the time of stimulation on modulating the brain’s activity is unknown. Therefore, in this project, in collaboration with the Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, researchers plan to find quantitative biomarkers based on advanced signal processing approaches to investigate modulations in brain’s electrical activity as a result of VEPs. This has implications for the role of cortical responses to stimuli in ongoing cortical activity, and for the dose calibration of VNS therapies.
Small Molecule Based HFG (Hepatocyte Growth Factor)- Mimetics as Potential Neuroprotective and Neurotrophic Agents
Hepatocyte growth factor (HGF) is a potent mitogen for hepatocytes that is required for liver development and regeneration. Therefore, identifying the most effective strategies to administer its biological effects in injured tissues is of high priority. In this study in collaborated with the Department of Pharmaceutical Sciences, University at Buffalo, we support the development of small-molecule hepatocyte growth factor (HGF) mimetics by providing structure-based in silico validation and evaluating ligand–receptor binding quality using AutoDock Tools molecular docking against the HGF/c-Met binding interface (PDB: 1BHT). We generate preliminary computational evidence to prioritize candidate compounds by assessing binding energy, docking pose stability, and clustering behavior across multiple docking runs. These analyses support the selection of lead boron-containing scaffolds and derivatives by identifying candidates with favorable predicted binding affinity and reproducible binding modes.
Developing and Evaluating an Earbud EEG System for ADHD
In this project, in collaboration with Niura, we are working on developing a novel earbud EEG system designed for monitoring cognitive and mental states, particularly targeting individuals with Attention-Deficit/Hyperactivity Disorder (ADHD). This system can 1) capture event-related potential and neural activity from within the ear canal, 2) enable continuous, real-time brain monitoring, and 3) provide a compact, wearable solution for home and school settings. The goal is to integrate this tool into feedback-based behavioral interventions and digital therapeutics for ADHD and related disorders.
Fast and robust microwave/inductive imaging algorithms for biomedical imaging and nondestructive evaluation
In this study, we contribute to the development of fast and robust 2D/3D microwave imaging methods for object detection and imaging in challenging environments. Because microwaves have relatively long wavelengths, they can penetrate dielectric media where visible light cannot, enabling imaging of obscured structures. Our work introduce efficient computational algorithms for microwave-based imaging with applications in biomedical imaging, concealed object detection, and nondestructive testing and evaluation, demonstrating improved resolution and accuracy compared with prior microwave imaging approaches. In addition, we developed an induction-based imaging framework for detecting unknown metallic objects at multiple depths, using lowfrequency electromagnetic fields that penetrate the medium and generate eddy currents on hidden metallic targets. This approach enables practical sensing in scenarios where conventional imaging methods are limited by opacity, occlusion, or material properties.
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