Research & Publication

Research Interest

My research focuses on advanced biomedical signal processing and statistical modeling for understanding the dynamics of brain function. I work extensively with EEG and fMRI data to characterise functional and effective brain connectivity, with a particular emphasis on modelling how neural networks evolve across time, cognitive states, and neuropsychiatric conditions.

A key theme of my work is the detection and interpretation of state changes in brain networks, including multilayer and community-structure representations across individuals. I am also interested in using machine learning and deep neural networks to classify brain connectivity patterns, particularly in disorders such as schizophrenia and motor imagery paradigms. More recent extensions include applications in neurological clinical analytics, EEG coherence analysis, and emerging intersections with digital health monitoring.

Broadly, my research sits at the intersection of biomedical engineering, computational neuroscience, and data-driven neurotechnology, aiming to advance diagnostic insight, neural decoding, and personalized neurohealth through interpretable connectivity modeling and intelligent signal analytics.