Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909813
V. Obradović, Ilija Rajak, M. Secujski, V. Delić
Online courses have had exponential growth during COVID-19 pandemic, and video lectures are also important for lifelong learning. However, lecturers experience a number of challenges in creating video lectures, related to both speech recording (microphone and noise; diction, articulation and intonation) and video recording (camera and light; consistency in appearance). It is particularly difficult to modify and update recorded content. The paper presents a solution for these problems based on the application of artificial intelligence in creating virtual speakers based on TTS synthesis and Wav2Lip GAN trained on a custom data set. A pilot project which included the evaluation and testing of the developed system by dozens of teachers will be presented in detail. The use of TTS overcomes the problems in achieving speaker consistency by providing high quality speech in different languages, while the attention and motivation of students is improved by using animated virtual speakers.
{"title":"Text driven virtual speakers","authors":"V. Obradović, Ilija Rajak, M. Secujski, V. Delić","doi":"10.23919/eusipco55093.2022.9909813","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909813","url":null,"abstract":"Online courses have had exponential growth during COVID-19 pandemic, and video lectures are also important for lifelong learning. However, lecturers experience a number of challenges in creating video lectures, related to both speech recording (microphone and noise; diction, articulation and intonation) and video recording (camera and light; consistency in appearance). It is particularly difficult to modify and update recorded content. The paper presents a solution for these problems based on the application of artificial intelligence in creating virtual speakers based on TTS synthesis and Wav2Lip GAN trained on a custom data set. A pilot project which included the evaluation and testing of the developed system by dozens of teachers will be presented in detail. The use of TTS overcomes the problems in achieving speaker consistency by providing high quality speech in different languages, while the attention and motivation of students is improved by using animated virtual speakers.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133625867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909777
Paschalis Tsiaflakis, W. Coomans
Reinforcement learning (RL) is a powerful machine learning technique to learn optimal actions in a control system setup. An important drawback of RL algorithms is the need for balancing exploitation vs exploration. Exploration corresponds to taking randomized actions with the aim to learn from it and make better decisions in the future. However, these exploratory actions result in poor performance, and current RL algorithms have a slow convergence as one can only learn from a single action outcome per iteration. We propose a novel concept of Inference-based RL that is applicable to a specific class of RL problems, and that allows to eliminate the performance impact caused by traditional exploration strategies, thereby making RL performance more consistent and greatly improving the convergence speed. The specific RL problem class is a problem class in which the observation of the outcome of one action can be used to infer the outcome of other actions, without the need to actually perform them. We apply this novel concept to the use case of dynamic resource allocation, and show that the proposed algorithm outperforms existing RL algorithms, yielding a drastic increase in both convergence speed and performance.
{"title":"Inference-based Reinforcement Learning and its Application to Dynamic Resource Allocation","authors":"Paschalis Tsiaflakis, W. Coomans","doi":"10.23919/eusipco55093.2022.9909777","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909777","url":null,"abstract":"Reinforcement learning (RL) is a powerful machine learning technique to learn optimal actions in a control system setup. An important drawback of RL algorithms is the need for balancing exploitation vs exploration. Exploration corresponds to taking randomized actions with the aim to learn from it and make better decisions in the future. However, these exploratory actions result in poor performance, and current RL algorithms have a slow convergence as one can only learn from a single action outcome per iteration. We propose a novel concept of Inference-based RL that is applicable to a specific class of RL problems, and that allows to eliminate the performance impact caused by traditional exploration strategies, thereby making RL performance more consistent and greatly improving the convergence speed. The specific RL problem class is a problem class in which the observation of the outcome of one action can be used to infer the outcome of other actions, without the need to actually perform them. We apply this novel concept to the use case of dynamic resource allocation, and show that the proposed algorithm outperforms existing RL algorithms, yielding a drastic increase in both convergence speed and performance.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133662619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909595
Vedran Mihal, B. Seifert, Markus Püschel
Directionality is an essential feature of many real-world networks, but problematic in graph signal processing (GSP) because there is no obvious choice of Fourier basis. In this work we investigate how to port GSP methods from undirected to directed graphs using recent work on graph signal denoising using trainable networks as a case study. We consider five notions of directed Fourier bases from the literature and different approaches for porting, from ad-hoc to conceptual. Our experimental results show that directionality does matter, the importance of a shift operator related to the chosen basis, and which directed Fourier basis may be best suited for applications. The best variant also provides a promising method for denoising signals on directed graphs.
{"title":"Porting Signal Processing from Undirected to Directed Graphs: Case Study Signal Denoising with Unrolling Networks","authors":"Vedran Mihal, B. Seifert, Markus Püschel","doi":"10.23919/eusipco55093.2022.9909595","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909595","url":null,"abstract":"Directionality is an essential feature of many real-world networks, but problematic in graph signal processing (GSP) because there is no obvious choice of Fourier basis. In this work we investigate how to port GSP methods from undirected to directed graphs using recent work on graph signal denoising using trainable networks as a case study. We consider five notions of directed Fourier bases from the literature and different approaches for porting, from ad-hoc to conceptual. Our experimental results show that directionality does matter, the importance of a shift operator related to the chosen basis, and which directed Fourier basis may be best suited for applications. The best variant also provides a promising method for denoising signals on directed graphs.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134526643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909671
Daniela Dapena, D. Lau, G. Arce
Efficient sampling of graph signals is essential to graph signal processing. Recently, blue-noise was introduced as a sampling method that maximizes the separation between sampling nodes leading to high-frequency dominance patterns, and thus, to high-quality patterns. Despite the simple inter-pretation of the method, blue-noise sampling is restricted to approximately regular graphs. This study presents an extension of blue-noise sampling that allows the application of the method to irregular graphs. Before sampling with a blue-noise algorithm, the approach regularizes the weights of the edges such that the graph represents a regular structure. Then, the resulting pattern adapts the node's distribution to the local density of the nodes. This work also uses an approach that minimizes the strength of the high-frequency components to recover approximately bandlimited signals. The experimental results show that the proposed methods have superior performance compared to the state-of-the-art techniques.
{"title":"Density Aware Blue-Noise Sampling on Graphs","authors":"Daniela Dapena, D. Lau, G. Arce","doi":"10.23919/eusipco55093.2022.9909671","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909671","url":null,"abstract":"Efficient sampling of graph signals is essential to graph signal processing. Recently, blue-noise was introduced as a sampling method that maximizes the separation between sampling nodes leading to high-frequency dominance patterns, and thus, to high-quality patterns. Despite the simple inter-pretation of the method, blue-noise sampling is restricted to approximately regular graphs. This study presents an extension of blue-noise sampling that allows the application of the method to irregular graphs. Before sampling with a blue-noise algorithm, the approach regularizes the weights of the edges such that the graph represents a regular structure. Then, the resulting pattern adapts the node's distribution to the local density of the nodes. This work also uses an approach that minimizes the strength of the high-frequency components to recover approximately bandlimited signals. The experimental results show that the proposed methods have superior performance compared to the state-of-the-art techniques.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133083426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909532
C. Herzet, Clément Elvira, H. Dang
We address the problem of safe screening for $ell_{1}$-penalized convex regression/classification problems, i.e., the identification of zero coordinates of the solutions. Unlike previous contributions of the literature, we propose a screening methodology which does not require the knowledge of a so-called “safe region”. Our approach does not rely on any other assumption than convexity (in particular, no strong-convexity hypothesis is needed) and therefore applies to a wide family of convex problems. When the Fenchel conjugate of the data-fidelity term is strongly convex, we show that the popular “GAP sphere test” proposed by Fercoq et al. can be recovered as a particular case of our methodology (up to a minor modification). We illustrate numerically the performance of our procedure on the “sparse support vector machine classification” problem.
{"title":"Region-free Safe Screening Tests for $ell_{1}$-penalized Convex Problems","authors":"C. Herzet, Clément Elvira, H. Dang","doi":"10.23919/eusipco55093.2022.9909532","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909532","url":null,"abstract":"We address the problem of safe screening for $ell_{1}$-penalized convex regression/classification problems, i.e., the identification of zero coordinates of the solutions. Unlike previous contributions of the literature, we propose a screening methodology which does not require the knowledge of a so-called “safe region”. Our approach does not rely on any other assumption than convexity (in particular, no strong-convexity hypothesis is needed) and therefore applies to a wide family of convex problems. When the Fenchel conjugate of the data-fidelity term is strongly convex, we show that the popular “GAP sphere test” proposed by Fercoq et al. can be recovered as a particular case of our methodology (up to a minor modification). We illustrate numerically the performance of our procedure on the “sparse support vector machine classification” problem.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133365478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909585
A. Hanif, M. Doroslovački
Simultaneous wireless information and power transfer (SWIPT) has the potential to realize the envisioned ubiquity of the internet of things (IoT) by energizing them wirelessly whilst exchanging information. Recently, low-complexity receiver architectures for SWIPT are being considered for decoding information from amplitude modulated signals after rectification. However, less attention is paid towards improving the non-linear rectifier model prevalent in these architectures which is often truncated till fourth-order term in diode characteristic. In this paper, a novel, tractable analytical model for the rectenna non-linearity is presented which provides a theoretical upper bound to harvested DC power over the amplitude shift keying (ASK) constellation space corresponding to the entire diode non-linear region. Besides, the work also exposes the convexity of harvested DC power vis-à-vis incoming signal power thereby verifying the rate-energy (R-E) tradeoff in SWIPT for different choices of transmitted symbol amplitude distributions. Finally, the theoretical results presented using the adopted model are substantiated with the Monte Carlo circuit simulations allowing to conveniently evaluate and draw compromise in SWIPT performance against a choice of modulation scheme out of the ASK constellation space.
{"title":"Amplitude Shift Keying Constellation Space for Simultaneous Wireless Information and Power Transfer","authors":"A. Hanif, M. Doroslovački","doi":"10.23919/eusipco55093.2022.9909585","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909585","url":null,"abstract":"Simultaneous wireless information and power transfer (SWIPT) has the potential to realize the envisioned ubiquity of the internet of things (IoT) by energizing them wirelessly whilst exchanging information. Recently, low-complexity receiver architectures for SWIPT are being considered for decoding information from amplitude modulated signals after rectification. However, less attention is paid towards improving the non-linear rectifier model prevalent in these architectures which is often truncated till fourth-order term in diode characteristic. In this paper, a novel, tractable analytical model for the rectenna non-linearity is presented which provides a theoretical upper bound to harvested DC power over the amplitude shift keying (ASK) constellation space corresponding to the entire diode non-linear region. Besides, the work also exposes the convexity of harvested DC power vis-à-vis incoming signal power thereby verifying the rate-energy (R-E) tradeoff in SWIPT for different choices of transmitted symbol amplitude distributions. Finally, the theoretical results presented using the adopted model are substantiated with the Monte Carlo circuit simulations allowing to conveniently evaluate and draw compromise in SWIPT performance against a choice of modulation scheme out of the ASK constellation space.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134073512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909949
Marzieh Ajirak, Yuhao Liu, P. Djurić
In this paper, we address the classification and dimensionality reduction via ensembles of Gaussian Process Latent Variable Models (GPLVMs). The underlying idea is to have a diverse representation of latent spaces represented by an ensemble of GPLVMs. Each GPLVM of the ensemble has its own projections of the high dimensional observed data on a low dimensional latent space. These models are weighted using importance sampling. Since in practical settings, neither the kernel of the GPLVM nor the dimension of the latent space is known, it is logical to engage an ensemble of GPLVMs based on different kernels and for each of them estimate the dimension of the lower dimensional space. We demonstrate the advantage of working with ensembles for classification and show the performance of dimensionality reduction of our method with numerical simulations.
{"title":"Ensembles of Gaussian process latent variable models","authors":"Marzieh Ajirak, Yuhao Liu, P. Djurić","doi":"10.23919/eusipco55093.2022.9909949","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909949","url":null,"abstract":"In this paper, we address the classification and dimensionality reduction via ensembles of Gaussian Process Latent Variable Models (GPLVMs). The underlying idea is to have a diverse representation of latent spaces represented by an ensemble of GPLVMs. Each GPLVM of the ensemble has its own projections of the high dimensional observed data on a low dimensional latent space. These models are weighted using importance sampling. Since in practical settings, neither the kernel of the GPLVM nor the dimension of the latent space is known, it is logical to engage an ensemble of GPLVMs based on different kernels and for each of them estimate the dimension of the lower dimensional space. We demonstrate the advantage of working with ensembles for classification and show the performance of dimensionality reduction of our method with numerical simulations.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124418968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909832
Aylin Tastan, Michael Muma, A. Zoubir
Block diagonal structure of the affinity matrix is advantageous, e.g. in graph-based cluster analysis, where each block corresponds to a cluster. However, constructing block diagonal affinity matrices may be challenging and computationally demanding. We propose a new eigenvalue-based block diagonal representation (EBDR) method. The idea is to estimate a block diagonal affinity matrix by finding an approximation to a vector of target eigenvalues. The target eigenvalues, which follow the ideal block-diagonal model, are efficiently determined based on a vector derived from the graph Laplacian that represents the blocks as a piece-wise linear function. The proposed EBDR shows promising performance compared to four optimally tuned state-of-the-art methods in terms of clustering accuracy and computation time using real-data examples.
{"title":"Eigenvalue-Based Block Diagonal Representation and Application to p-Nearest Neighbor Graphs","authors":"Aylin Tastan, Michael Muma, A. Zoubir","doi":"10.23919/eusipco55093.2022.9909832","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909832","url":null,"abstract":"Block diagonal structure of the affinity matrix is advantageous, e.g. in graph-based cluster analysis, where each block corresponds to a cluster. However, constructing block diagonal affinity matrices may be challenging and computationally demanding. We propose a new eigenvalue-based block diagonal representation (EBDR) method. The idea is to estimate a block diagonal affinity matrix by finding an approximation to a vector of target eigenvalues. The target eigenvalues, which follow the ideal block-diagonal model, are efficiently determined based on a vector derived from the graph Laplacian that represents the blocks as a piece-wise linear function. The proposed EBDR shows promising performance compared to four optimally tuned state-of-the-art methods in terms of clustering accuracy and computation time using real-data examples.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124948598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909709
Evangelos Vlachos, C. Mavrokefalidis, K. Berberidis
The incorporation of UAVs in 5G and envisioned 6G wireless communication systems is considered for many applications and use-cases, either as part of the infrastructure, providing coverage and connectivity (e.g., during unforeseen and rare events) or as an end-user, e.g., in remote sensing, real-time monitoring and surveillance, to name a few. From the perspective of the physical layer and the involved signal processing algorithms, the transmission environment between the UAVs and the ground communication devices, along with the utilisation of massive MIMO in the mmWave spectrum, require new channel estimation algorithms to support the required physical layer functionality. In this paper, the problem of channel estimation in a multi-user, UAV-based mmWave massive MIMO system is considered in view of the so-called beam squint effect as well as the time-varying nature of the involved channels due to mobility. The proposed approach takes advantage of the low-rank channel matrix and solves a minimisation problem via ADMM, leading to a low complexity, iterative algorithm. The performance of the proposed algorithm is evaluated via simulations and its efficacy is demonstrated over other algorithms from the relevant literature.
{"title":"Channel Estimation for UAV-based mmWave Massive MIMO Communications with Beam Squint","authors":"Evangelos Vlachos, C. Mavrokefalidis, K. Berberidis","doi":"10.23919/eusipco55093.2022.9909709","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909709","url":null,"abstract":"The incorporation of UAVs in 5G and envisioned 6G wireless communication systems is considered for many applications and use-cases, either as part of the infrastructure, providing coverage and connectivity (e.g., during unforeseen and rare events) or as an end-user, e.g., in remote sensing, real-time monitoring and surveillance, to name a few. From the perspective of the physical layer and the involved signal processing algorithms, the transmission environment between the UAVs and the ground communication devices, along with the utilisation of massive MIMO in the mmWave spectrum, require new channel estimation algorithms to support the required physical layer functionality. In this paper, the problem of channel estimation in a multi-user, UAV-based mmWave massive MIMO system is considered in view of the so-called beam squint effect as well as the time-varying nature of the involved channels due to mobility. The proposed approach takes advantage of the low-rank channel matrix and solves a minimisation problem via ADMM, leading to a low complexity, iterative algorithm. The performance of the proposed algorithm is evaluated via simulations and its efficacy is demonstrated over other algorithms from the relevant literature.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132344428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909757
A. Griffa, M. Preti
The relation between brain functional activity and the underlying structure is complex and varies depending on the specific brain region. Recently, we used graph signal processing to introduce the structural-decoupling index (SDI), a novel metric quantifying structure-function coupling in brain regions, based on graph spectral filtering of functional activity. At slow temporal scales accessible with resting-state functional magnetic resonance imaging, the SDI showed a meaningful spatial gradient from unimodal (more coupled) to transmodal regions (more liberal). It also showed to perform very well for brain fingerprinting; i.e., individuals could be classified with near perfect accuracy based on their SDI. Here, we investigate structure-function coupling at faster temporal scales and its specificity to individuals, by means of resting-state magnetoencephalography (MEG) of 84 healthy subjects. We found that the MEG SDI forms a cortical gradient from task-positive regions, more coupled, to task-negative regions, highly decoupled. Great specificity of the SDI to individuals was confirmed, with largest subject classification accuracies in the beta and alpha bands. We conclude that structure-function coupling changes across temporal scales of investigation and provides rich signatures of individual brain organization at rest.
{"title":"Brain structure-function coupling is unique to individuals across multiple frequency bands: a graph signal processing study","authors":"A. Griffa, M. Preti","doi":"10.23919/eusipco55093.2022.9909757","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909757","url":null,"abstract":"The relation between brain functional activity and the underlying structure is complex and varies depending on the specific brain region. Recently, we used graph signal processing to introduce the structural-decoupling index (SDI), a novel metric quantifying structure-function coupling in brain regions, based on graph spectral filtering of functional activity. At slow temporal scales accessible with resting-state functional magnetic resonance imaging, the SDI showed a meaningful spatial gradient from unimodal (more coupled) to transmodal regions (more liberal). It also showed to perform very well for brain fingerprinting; i.e., individuals could be classified with near perfect accuracy based on their SDI. Here, we investigate structure-function coupling at faster temporal scales and its specificity to individuals, by means of resting-state magnetoencephalography (MEG) of 84 healthy subjects. We found that the MEG SDI forms a cortical gradient from task-positive regions, more coupled, to task-negative regions, highly decoupled. Great specificity of the SDI to individuals was confirmed, with largest subject classification accuracies in the beta and alpha bands. We conclude that structure-function coupling changes across temporal scales of investigation and provides rich signatures of individual brain organization at rest.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131936178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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