Computational neuroscience in clinical applications

have worked on developing neurophysiological biomarkers derived from spectral power and temporal indices of electroencephalography (EEG) to create algorithms for objective pain measurement. Moving beyond conventional verbal pain reports used in current clinical practice, this work has aimed to help physicians assess patients’ pain perception more objectively by detecting pain severity through biomarker-based models. This addresses a major clinical problem—the subjectivity and inaccuracy of pain evaluation—which can contribute to inappropriate analgesic prescribing and suboptimal pain management. Prior to algorithm development, my work focused on localising cortical brain regions involved in chronic pain processing and characterising the oscillatory features underlying pain perception. Given that chronic pain is influenced by multiple factors, including educational background, cognitive capacity, emotional state and timing of assessment, our research has also identified factors affecting pain perception, suggested more appropriate timing for pain evaluation, and delineated brain oscillatory signatures associated with pain processing. Together, this work contributes both mechanistic insight and practical solutions toward objective pain assessment.