EEG and MEG capture brain activity with remarkable precision, opening up possibilities far beyond traditional neuroscience. My work explores how these signals can be translated into commands, used to monitor brain function, and leveraged to better understand and diagnose neurological conditions. By bridging my BCI expertise with clinical applications, I am building toward a unified framework for detecting, tracking, and relieving neurological symptoms through intelligent neural interfaces.
Most BCI systems rely on simple, localized brain signals, but the brain is anything but simple or localized. I am exploring a richer class of brain dynamics known as neuronal avalanches: brief, spontaneous bursts of activity that ripple across the cortex and carry surprisingly rich information about cognitive and motor states. By incorporating these patterns into closed-loop BCI design, I aim to build systems that are more in tune with how the brain actually works.
Why do some people excel at controlling a BCI while others struggle? Part of the answer lies in how the brain adapts during training. I study the neurophysiological changes that unfold as users learn to modulate their own brain activity through BCI and neurofeedback, with the goal of making these systems more effective and personalized.
Better algorithms alone won't solve BCI's biggest challenges, we also need better features. I develop decoding approaches that combine multiple signal types and measurement modalities, painting a fuller picture of a user's mental state and improving the reliability and accuracy of neural interfaces.
I contribute to the development (and now to the use) of helium-4 optically pumped magnetometers (OPMs), a new generation of cryogen-free sensors for measuring the brain's and heart's magnetic fields. Lighter, more flexible, and easier to use than traditional systems, these sensors have the potential to make high-quality neuroimaging accessible in entirely new settings.