Lukas Hecker

The paper introduces a benchmark and Python library for EEG inverse solvers, showing no single method dominates all scenarios and highlighting the performance of flexible subspace and hybrid methods.

The paper introduces PATCH-AP, a source localization method for MEG that outperforms existing techniques in localizing both discrete and extended neural sources.

This paper introduces FLEX-AP, an enhanced alternating projection method for localizing spatially extended brain sources, showing improved accuracy over existing methods.

This paper introduces FLEX-MUSIC, a method for localizing neural sources in M/EEG data that also estimates source extent, achieving high accuracy and efficiency.

The paper investigates EEG correlates of perceptual reversals in Necker cubes, finding early neural destabilization before spontaneous reversals, suggesting they are not as spontaneous as previously thought.

This paper evaluates LSTM networks for M/EEG source imaging using simulated and real EEG data, finding higher accuracy compared to classical methods and alternative architectures.

ConvDip is a convolutional neural network that solves the EEG inverse problem using a distributed dipole model, outperforming existing methods in accuracy and efficiency.