42 research outputs found
Graph Neural Network-based EEG Classification:A Survey
Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.</p
Graph Neural Network-based EEG Classification: A Survey
Graph neural networks (GNN) are increasingly used to classify EEG for tasks
such as emotion recognition, motor imagery and neurological diseases and
disorders. A wide range of methods have been proposed to design GNN-based
classifiers. Therefore, there is a need for a systematic review and
categorisation of these approaches. We exhaustively search the published
literature on this topic and derive several categories for comparison. These
categories highlight the similarities and differences among the methods. The
results suggest a prevalence of spectral graph convolutional layers over
spatial. Additionally, we identify standard forms of node features, with the
most popular being the raw EEG signal and differential entropy. Our results
summarise the emerging trends in GNN-based approaches for EEG classification.
Finally, we discuss several promising research directions, such as exploring
the potential of transfer learning methods and appropriate modelling of
cross-frequency interactions.Comment: 14 pages, 3 figure
Roll plus maneuver load alleviation control system designs for the active flexible wing wind-tunnel model
Three designs for controlling loads while rolling for the Active Flexible Wing (AFW) are discussed. The goal is to provide good roll control while simultaneously limiting the torsion and bending loads experienced by the wing. The first design uses Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/LTR) modern control methods to control roll rate and torsional loads at four different wing locations. The second design uses a nonlinear surface command function to produce surface position commands as a function of current roll rate and commanded roll rate. The final design is a flutter suppression control system. This system stabilizes both symmetric and axisymmetric flutter modes of the AFW
Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity:A Study of Alzheimer's Disease
Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals, such as electroencephalography (EEG) recordings, into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the reconstruction of a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases in δ-θ coupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.UnknownSupports Open Acces
EEG-based Graph Neural Network Classification of Alzheimer's Disease:An Empirical Evaluation of Functional Connectivity Methods
Alzheimer’s disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised, as each aims to quantify a unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While a growing number of studies use GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy, whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy
Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease using EEG Data
Graph neural network (GNN) models are increasingly being used for the
classification of electroencephalography (EEG) data. However, GNN-based
diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains
a relatively unexplored area of research. Previous studies have relied on
functional connectivity methods to infer brain graph structures and used simple
GNN architectures for the diagnosis of AD. In this work, we propose a novel
adaptive gated graph convolutional network (AGGCN) that can provide explainable
predictions. AGGCN adaptively learns graph structures by combining
convolution-based node feature enhancement with a correlation-based measure of
power spectral density similarity. Furthermore, the gated graph convolution can
dynamically weigh the contribution of various spatial scales. The proposed
model achieves high accuracy in both eyes-closed and eyes-open conditions,
indicating the stability of learned representations. Finally, we demonstrate
that the proposed AGGCN model generates consistent explanations of its
predictions that might be relevant for further study of AD-related alterations
of brain networks.Comment: 16 pages, 16 figure
Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis
Alzheimer's disease (AD) is one of the most common neurodegenerative
diseases, with around 50 million patients worldwide. Accessible and
non-invasive methods of diagnosing and characterising AD are therefore urgently
required. Electroencephalography (EEG) fulfils these criteria and is often used
when studying AD. Several features derived from EEG were shown to predict AD
with high accuracy, e.g. signal complexity and synchronisation. However, the
dynamics of how the brain transitions between stable states have not been
properly studied in the case of AD and EEG data. Energy landscape analysis is a
method that can be used to quantify these dynamics. This work presents the
first application of this method to both AD and EEG. Energy landscape assigns
energy value to each possible state, i.e. pattern of activations across brain
regions. The energy is inversely proportional to the probability of occurrence.
By studying the features of energy landscapes of 20 AD patients and 20 healthy
age-matched counterparts, significant differences were found. The dynamics of
AD patients' brain networks were shown to be more constrained - with more local
minima, less variation in basin size, and smaller basins. We show that energy
landscapes can predict AD with high accuracy, performing significantly better
than baseline models.Comment: 11 pages, 7 figure
Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma
Burkitt lymphoma (BL) is the most common B-cell lymphoma in children. Within the International Cancer Genome Consortium (ICGC), we performed whole genome and transcriptome sequencing of 39 sporadic BL. Here, we unravel interaction of structural, mutational, and transcriptional changes, which contribute to MYC oncogene dysregulation together with the pathognomonic IG-MYC translocation. Moreover, by mapping IGH translocation breakpoints, we provide evidence that the precursor of at least a subset of BL is a B-cell poised to express IGHA. We describe the landscape of mutations, structural variants, and mutational processes, and identified a series of driver genes in the pathogenesis of BL, which can be targeted by various mechanisms, including IG-non MYC translocations, germline and somatic mutations, fusion transcripts, and alternative splicing
The genomic and transcriptional landscape of primary central nervous system lymphoma
Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Molecular drivers of PCNSL have not been fully elucidated. Here, we profile and compare the whole-genome and transcriptome landscape of 51 CNS lymphomas (CNSL) to 39 follicular lymphoma and 36 DLBCL cases outside the CNS. We find recurrent mutations in JAK-STAT, NFkB, and B-cell receptor signaling pathways, including hallmark mutations in MYD88 L265P (67%) and CD79B (63%), and CDKN2A deletions (83%). PCNSLs exhibit significantly more focal deletions of HLA-D (6p21) locus as a potential mechanism of immune evasion. Mutational signatures correlating with DNA replication and mitosis are significantly enriched in PCNSL. TERT gene expression is significantly higher in PCNSL compared to activated B-cell (ABC)-DLBCL. Transcriptome analysis clearly distinguishes PCNSL and systemic DLBCL into distinct molecular subtypes. Epstein-Barr virus (EBV)+ CNSL cases lack recurrent mutational hotspots apart from IG and HLA-DRB loci. We show that PCNSL can be clearly distinguished from DLBCL, having distinct expression profiles, IG expression and translocation patterns, as well as specific combinations of genetic alterations