Graph Signal Processing of Low and High-Order Dynamic Functional Connectivity Networks Using EEG Resting-State for Schizophrenia: A Whole Brain Breakdown

Dimitriadis, Stavros I.

doi:10.1101/551671

Cited by 4 publications

(3 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent studies have utilized graph-based frameworks and GSP perspectives, enhancing our understanding of brain phenomena and interconnectivity among brain regions. These investigations have provided insights into brain signal classification [18,19], diagnosis of brain diseases [20][21][22][23], BCI [24][25][26], and graph frequency analysis of brain signals [27].…”

Section: Introductionmentioning

confidence: 99%

A Dimensionality Reduction Approach for Motor Imagery BCI using Functional Clustering, Graph Signal Processing, and Differential Evolution

Khalili,

Abootalebi,

Saeedi-Sourck

2023

Preprint

View full text Add to dashboard Cite

This paper aims to address the dimension reduction and classification of electroencephalogram (EEG) signals within the context of motor imagery brain-computer interface (MI-BCI). By leveraging modern brain signal processing tools, specifically Graph Signal Processing (GSP) and meta-heuristic techniques, we introduce the K-GLR-DE approach. This methodology encompasses functional clustering, Kron reduction, regularized common spatial patterns with generic learning (GLRCSP), and differential evolution (DE). Our approach is underpinned by a comprehensive structural-functional framework that carefully shapes the architecture of the brain graph. Edge weights are assigned based on geometric distance and correlation, imbuing the model with physiologically meaningful connectivity patterns. Graph reduction involves strategically employing physiological regions of interest (ROIs) and Kron reduction to select informative subgraphs while preserving vital information from all graph vertices. Feature extraction integrates total variation calculation and the GLRCSP method, followed by dimension reduction using the DE algorithm. The extracted features are then evaluated using well-established machine-learning classifiers. The validation process is carried out using Dataset IVa from BCI Competition III, providing a tangible benchmark for the performance of the K-GLR-DE approach. Significantly, the SVM-RBF classifier stands out as the top performer, achieving a remarkable average accuracy of 96.46±0.83. Noteworthy is our approach’s capacity to notably augment MI-BCI classification performance across diverse training trial scenarios, encompassing limited, small, and conventional settings.

show abstract

Section: Introductionmentioning

confidence: 99%

A Dimensionality Reduction Approach for Motor Imagery BCI using Functional Clustering, Graph Signal Processing, and Differential Evolution

Khalili,

Abootalebi,

Saeedi-Sourck

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A recent survey of artificial intelligence methods for the classification and detection of Schizophrenia (Lai et al, 2021), shows that MKL has been applied to both structural and functional Magnetic Resonance Images (MRI), increasing performance accuracy (Ulaş et al, 2012;Castro et al, 2014;Iwabuchi and Palaniyappan, 2017). Nevertheless, in this review MKL algorithms applied to electrophysiological data have been not reported, although a recent study used EEG dynamic functional connectivity networks to classify SZ based on MKL (Dimitriadis, 2019). To our knowledge, ERP data has not been used to classify SZ using MKL despite its use for other purposes such as brain-computer interfaces (Li et al, 2014;Yoon and Kim, 2017).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Febles

Ortega

Sosa

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

AntecedentThe event-related potential (ERP) components P300 and mismatch negativity (MMN) have been linked to cognitive deficits in patients with schizophrenia. The diagnosis of schizophrenia could be improved by applying machine learning procedures to these objective neurophysiological biomarkers. Several studies have attempted to achieve this goal, but no study has examined Multiple Kernel Learning (MKL) classifiers. This algorithm finds optimally a combination of kernel functions, integrating them in a meaningful manner, and thus could improve diagnosis.ObjectiveThis study aimed to examine the efficacy of the MKL classifier and the Boruta feature selection method for schizophrenia patients (SZ) and healthy controls (HC) single-subject classification.MethodsA cohort of 54 SZ and 54 HC participants were studied. Three sets of features related to ERP signals were calculated as follows: peak related features, peak to peak related features, and signal related features. The Boruta algorithm was used to evaluate the impact of feature selection on classification performance. An MKL algorithm was applied to address schizophrenia detection.ResultsA classification accuracy of 83% using the whole dataset, and 86% after applying Boruta feature selection was obtained. The variables that contributed most to the classification were mainly related to the latency and amplitude of the auditory P300 paradigm.ConclusionThis study showed that MKL can be useful in distinguishing between schizophrenic patients and controls when using ERP measures. Moreover, the use of the Boruta algorithm provides an improvement in classification accuracy and computational cost.

show abstract

“…Electroencephalography (EEG) on the other hand provides a reasonable alternative with -albeit lower spatial, but -much higher temporal resolution, thus allowing for a more detailed reconstruction of network dynamics. Despite this and other advantages of EEG imaging (i.e., its accessibility and mobility), up to date not many studies have used dynamic graph analysis of electrophysiological recordings to investigate DFC in SZ (Dimitriadis, 2019).…”

Section: Introductionmentioning

confidence: 99%

Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia

Racz

Stylianou

Mukli

et al. 2020

Front. Syst. Neurosci.

View full text Add to dashboard Cite

Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactionssuch as their multifractality or information content-, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age-and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5-4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger longrange autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.

show abstract

Graph Signal Processing of Low and High-Order Dynamic Functional Connectivity Networks Using EEG Resting-State for Schizophrenia: A Whole Brain Breakdown

Cited by 4 publications

References 51 publications

A Dimensionality Reduction Approach for Motor Imagery BCI using Functional Clustering, Graph Signal Processing, and Differential Evolution

A Dimensionality Reduction Approach for Motor Imagery BCI using Functional Clustering, Graph Signal Processing, and Differential Evolution

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials

Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia

Contact Info

Product

Resources

About