My research is about denoising electromagnetic brain recordings with the help of deep neural networks.
Electromagnetic recordings are multi-sensor time-series data and inherently contaminated with noise.
The noise can be divided into two major categories, namely sensor noise which is independent for each sensor, and environmental noise which is correlated across the sensors.
The recorded data is therefore a mixture of brain activity (signal of interest), sensor noise and environmental noise and can be decomposed into its components. While commonly used frameworks are based on linear decomposition methods like SVD, PCA and ICA, non-linear methods like deep neural networks exceed their capability..
I develop self-supervised deep neural network architectures specifically for the use case of electromagnetic recordings where ground truth is not available.