Research focus

01   Real-time decoding and brain–computer interfaces

We investigate how ongoing neural signals can support timely estimates of a person's state or intention. Research questions include reliable decoding, adaptation across sessions and users, and the effects of signal quality and processing delay on interactive performance.

02   Neurofeedback and self-regulation

We study feedback that helps participants observe and engage with selected features of their own brain activity. By relating feedback, regulation strategies, and behavioral measures, we ask what participants learn and how neural patterns change during the interaction.

03   Neuromodulation and adaptive intervention

We explore how targeted interventions can be integrated with neural measurements to investigate brain responses. A central question is how the current brain state should inform the timing and parameters of an intervention, and how subsequent neural and behavioral changes should be evaluated.

How we investigate

Our conceptual workflow is to measure brain activity, estimate a relevant state, deliver feedback or an experimental intervention, and measure the response. We connect EEG-based BCI, real-time fMRI, neurofeedback, and neuromodulation within this framework. Controlled comparisons and repeated measurements help us distinguish immediate responses, learned regulation, and changes that persist beyond the feedback period.

Across our research framework

Closed-loop research brings computational models back to the brain. Observed responses can refine our accounts of neural representation, test the usefulness of pretrained decoders, and motivate AI systems that adapt through ongoing interaction.

BCI · Real-time decoding · Neurofeedback · Neuromodulation