Research Project
Resting-State EEG Biomarkers of Depression Severity and Sleep Quality: The Role of Frontal Alpha and Theta
This study investigates whether resting-state EEG biomarkers can reflect depression severity and sleep quality. The project focuses on frontal brain asymmetry as a potential neural indicator of emotional and physiological dysregulation
EEG biomarkers of depression and sleep, focusing on how resting-state brain activity reflects emotional and physiological dysregulation. Using frontal alpha and theta asymmetry, the study examines the relationship between neural activity, depression severity, and sleep quality.
This work provides a foundation for broader brain disorder research, as depression and sleep disturbances are increasingly recognised as early indicators of underlying neurological dysfunction. The findings highlight the role of EEG as an objective tool while emphasising the need for integrative approaches combining neural, behavioural, and genetic data.
Methds, Key Findings, Research Insight
Methods
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64-channel EEG (resting-state: eyes open & closed)
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Frontal Alpha Asymmetry (FAA)
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Frontal Theta Asymmetry (FTA)
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Psychological measures (BDI-II, PSQI)
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Asymmetry Index Calculation= In(R) – In(L)
Key Findings
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Depression severity is linked to EEG alpha asymmetry
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Sleep disturbance is strongly associated with depression
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EEG markers did not fully explain the depression–sleep relationship
Research Insight
EEG biomarkers capture specific aspects of brain function but are not sufficient alone to explain complex brain disorders. This highlights the need for integrative approaches combining neural, genetic, and behavioural data.
Future Research: Brain Disorders & Neurogenetics
My future research will focus on the integration of EEG biomarkers and neurogenetics to investigate early brain dysfunction across major neurological and neurodevelopmental disorders. Priority will be given to conditions requiring urgent scientific and clinical attention, including Parkinson’s disease, Alzheimer’s disease, Autism Spectrum Disorder, and Epilepsy.
This work will examine how genetic risk factors influence brain activity, connectivity, and early neural markers before clinical symptoms emerge. Key areas include the identification of early biomarkers, gene–brain connectivity relationships, and mechanisms such as neuroinflammation and mitochondrial dysfunction.
A central aim is to contribute to precision neuroscience by linking genetic variation to neural signatures and behavioural outcomes, supporting early detection and targeted intervention strategies. Future directions also include the exploration of gene-based and epigenetic therapies, including gene silencing, microRNA approaches, and AI-driven neurogenetic modelling.