Examination of the Calibrated Stop-Signal Task with revised EEG methodology and individualised analyses

This research project has the potential to change how we understand and diagnose anxiety, one of the most widespread mental health challenges in today’s society. Using EEG technology to measure brain activity, we aim to identify a unique pattern – called a biomarker – that could indicate when someone is experiencing a specific type of anxiety known as Goal Conflict anxiety. This form of anxiety occurs when people feel stuck between competing goals or decisions: something many experience but is hard to detect or measure objectively.

Right now, anxiety is diagnosed mainly through self-report and clinical interviews. But what if we could back that up with objective brain data? This study is an important step toward that future. If successful, it could lead to faster, more accurate, and more personalised diagnosis – helping people get support earlier and reducing the stigma around mental health by showing it has a measurable biological basis.

We’re also taking a personalised approach to analysing the brain activity data, recognising that each person’s brain is different. By embracing that diversity rather than averaging it out, we hope to develop insights that reflect how anxiety actually shows up in real people, not just in theory.

This kind of research has wide-reaching benefits: it could influence how doctors, psychologists, and even schools support people struggling with anxiety. Over time, it could reduce pressure on healthcare systems by making early intervention easier and more precise.