Sofie Therese Hansen

TL;DR: This work proposes a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models, combined with only a recorded EEG signal, and demonstrates that personalized EEG brain imaging is possible, even when theHead geometry and conductivities are unknown.

TL;DR: Combining source localization and EMD in the pursuit of further insight into the mechanisms of the brain with respect to the phase and frequency of the electrical oscillations and their cortical origin are encouraged.

TL;DR: The method is named the MarkoVG and its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data is demonstrated and a benchmark EEG dataset is used to demonstrate the method's ability to recover non‐stationary brain dynamics.

TL;DR: It is shown that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar, and the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding.

TL;DR: In this article, the authors used scalp electroencephalography (EEG) to study the brain's ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011).