Sparse Multi-task Inverse Covariance Estimation for Connectivity Analysis in EEG Source Space

Abstract

Understanding how different brain areas interact to generate complex behavior is a primary goal of neuroscience research. One approach, functional connectivity analysis, aims to characterize the connectivity patterns within brain networks. In this paper, we address the problem of discriminative connectivity, i.e. determining the differences in network structure under different experimental conditions. We introduce a novel model called Sparse Multi-task Inverse Covariance Estimation (SMICE) which is capable of estimating a common connectivity network as well as discriminative networks across different tasks. We apply the method to EEG signals after solving the inverse problem of source localization, yielding networks defined on the cortical surface. We propose an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve SMICE. We apply our newly developed framework to find common and discriminative connectivity patterns for α-oscillations during the Sleep Onset Process (SOP) and during Rapid Eye Movement (REM) sleep. Even though both stages exhibit a similar α-oscillations, we show that the underlying networks are distinct.

A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging

“…The study of ESI is of great significance in both neuroscience and clinical applications ( Congedo and Sherlin, 2011 ). Accurate estimation of brain sources can not only help neuroscientists to better understand the brain mechanism ( Liu et al, 2019 ) and the pathological characteristics of brain injury or mental disorders ( da Silva, 2013 ), but also help doctors to identify the lesion areas of brain diseases such as epilepsy focal regions, which can contribute to the improvement of the accuracy of presurgical evaluations ( Sanei and Chambers, 2013 ).…”

Akzidentelle Wachheit während Allgemeinanästhesie

“…Die Verbindung zwischen Narkosetiefe, Bewusstheit, Erinnerungsbildung und EEG-Signalanalyse ist durch heutige DoA-Monitoring-Verfahren noch nicht hergestellt. Allerdings gibt es faszinierende Untersuchungen, die möglicherweise den Zusammenhang zwischen Bewusstsein und Anästhesietiefe durch Musterveränderung im EEG darstellen können [8,44,45,46].…”

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs

“…This is primarily because the correlation matrix can depict the interactions and cooperative activities among different brain regions (Liu and Ye, 2023 ). It has been demonstrated that due to the non-uniform connectivity patterns in the brain, the correlation matrix of EEG signals is typically sparse (Liu et al, 2019 ). For example, Figure 1 exemplifies the correlation matrix of 62 EEG channels utilized in a fatigue detection task with a classification accuracy of 94%.…”