Time-varying model identification for time-frequency feature extraction from EEG data

A novel modelling scheme that can be used to estimate and track time-varying properties of nonstationary signals is investigated. This scheme is based on a class of time-varying AutoRegressive with an eXogenous input (ARX) models where the associated time-varying parameters are represented by multi-wavelet basis functions. The orthogonal least square (OLS) algorithm is then applied to refine the model parameter estimates of the time-varying ARX model. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. Simulation studies and applications to real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of nonstationary processes

Detecting and tracking time-varying causality with applications to EEG data

This paper introduces a novel method called the ERR-Causality, or Error Reduction Ratio Causality test, that can be used to detect and track causal relationships between two signals using a new adaptive forward orthogonal least squares (Adaptive-Forward-OLS) algorithm. In comparison to the traditional Granger method, one advantage of the new ERR-Causality test is that it can effectively detect the time-varying direction of linear or nonlinear causality between two signals without fitting a complete model. Another important advantage is that the ERR-Causality test can detect both the direction of interactions and estimate the relative time shift between the two signals. Several numerical examples are provided to illustrate the effectiveness of the new method for causal relationship detection between two signals. An important real application, relating to the analysis of the causality of EEG signals from different cortical sites which can be very useful for understanding brain activity during an epileptic seizure by inspecting the high-resolution time varying directed information flow, is also discussed

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. A new efficient Common Model Structure Selection (CMSS) algorithm is proposed to select a common model structure. The main idea and key procedure is: First, generate K 1 data sets (the first K data sets are used for training, and theK 1 th one is used for testing) using an online sliding window method; then detect significant model terms to form a common model structure which fits over all the K training data sets using the new proposed CMSS approach. Finally, estimate and refine the time-varying parameters for the identified common-structured model using a Recursive Least Squares (RLS) parameter estimation method. The new method can effectively detect and adaptively track the transient variation of nonstationary signals. Two examples are presented to illustrate the effectiveness of the new approach including an application to an EEG data set

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

Fog-enabled Scalable C-V2X Architecture for Distributed 5G and Beyond Applications

The Internet of Things (IoT) ecosystem, as fostered by fifth generation (5G) applications, demands a highly available network infrastructure. In particular, the internet of vehicles use cases, as a subset of the overall IoT environment, require a combination of high availability and low latency in big volumes support. This can be enabled by a network function virtualization architecture that is able to provide resources wherever and whenever needed, from the core to the edge up to the end user proximity, in accordance with the fog computing paradigm. In this article, we propose a fog-enabled cellular vehicle-to-everything architecture that provides resources at the core, the edge and the vehicle layers. The proposed architecture enables the connection of virtual machines, containers and unikernels that form an application-as-a-service function chain that can be deployed across the three layers. Furthermore, we provide lifecycle management mechanisms that can efficiently manage and orchestrate the underlying physical resources by leveraging live migration and scaling functionalities. Additionally, we design and implement a 5G platform to evaluate the basic functionalities of our proposed mechanisms in real-life scenarios. Finally, the experimental results demonstrate that our proposed scheme maximizes the accepted requests, without violating the applications’ service level agreement.This work has been supported in part by the research projects SPOTLIGHT (722788), AGAUR (2017-SGR-891), 5G-DIVE (859881), SPOT5G (TEC2017-87456-P), MonB5G (871780) and 5G-Routes (951867)

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

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

Safety, tolerability, and nocebo phenomena during transcranial magnetic stimulation: a systematic review and meta‐analysis of placebo‐controlled clinical trials

Background The methodology used for the application of repetitive transcranial magnetic stimulation (TMS) is such that it may induce a placebo effect. Respectively, adverse events (AEs) can occur when using a placebo, a phenomenon called nocebo. The primary aim of our meta‐analysis is to establish the nocebo phenomena during TMS. Safety and tolerability of TMS were also studied. Methods After a systematic Medline search for TMS randomized controlled trials (RCTs), we assessed the number of patients reporting at least one AE and the number of discontinuations because of AE in active and sham TMS groups. Results Data were extracted from 93 RCTs. The overall pooled estimate of active TMS and placebo treated patients who discontinued treatment because of AEs was 2.5% (95% CI 1.9%‐3.2%) and 2.7% (95% CI 2.0%‐3.5%), respectively. The pooled estimate of active TMS and placebo treated patients experiencing at least one AE was 29.3% (95% CI 19.0%‐22.6%) and 13.6% (95% CI 11.6%‐15.8%), respectively, suggesting that the odds of experiencing an AE is 2.60 times higher (95% CI 1.75‐3.86) in the active treatment group compared to placebo (p < 0.00001). The most common AE was headache, followed by dizziness. Secondary meta‐analyses in depression and psychotic disorders showed that the odds of experiencing an AE is 3.98 times higher (95% CI 2.14‐7.40) and 2.93 times higher (95% CI 1.41‐6.07), respectively, in the active treatment groups compared to placebo. Conclusions TMS is a safe and well‐tolerated intervention. Nocebo phenomena do occur during TMS treatment and should be acknowledged during clinical trial design and daily clinical practice