Results 11 to 20 of about 5,709,929 (152)

GSP Cochlea: A graph signal processing approach for studying sound encoding [PDF]

PNAS Nexus
Abstract Humans are able to hear in a variety of complicated acoustic environments. This feat begins in the peripheral auditory system, where the cochlea collects and transmits thousands of individual bits of sound data to the brain. Here, we introduce GSP Cochlea: a graph signal processing-based framework to investigate and visualize
Melia E Bonomo, Santiago Segarra, Robert M Raphael   +2 more
openaire   +4 more sources

A joint range–angle–velocity estimation algorithm for FDA-MIMO radar based on graph signal processing [PDF]

Scientific Reports
In this paper, a novel Frequency Diverse Array–Multiple Input Multiple Output (FDA-MIMO) radar parameter estimation algorithm based on Graph Signal Processing (GSP) is proposed for joint range–angle–velocity estimation.
Qinlin Li, Ao Meng, Kefei Liao, Ningbo Xie, Xianglai Liao, Hanbo Chen, Kamarul Hawari Bin Ghazali   +6 more
doaj   +2 more sources

RETRACTED ARTICLE: Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder

Scientific Reports
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition associated with disrupted brain connectivity. Traditional graph-theoretical approaches have been widely employed to study ASD biomarkers; however, these methods are often limited to
Ayesha Jabbar, Huang Jianjun, Muhammad Kashif Jabbar, Khalil ur Rehman, Anas Bilal   +4 more
doaj   +2 more sources

Topological Signal Processing from Stereo Visual SLAM [PDF]

Sensors
Topological signal processing is emerging alongside Graph Signal Processing (GSP) in various applications, incorporating higher-order connectivity structures—such as faces—in addition to nodes and edges, for enriched connectivity modeling.
Eleonora Di Salvo, Tommaso Latino, Maria Sanzone, Alessia Trozzo, Stefania Colonnese   +4 more
doaj   +2 more sources

Multimodal Brain Graph Analysis for Uncovering Path-Based Biomarkers in Schizophrenia: Integrating Static and Dynamic Approaches. [PDF]

Hum Brain Mapp
A multimodal framework integrating time‐resolved fMRI connectivity and gray matter features enables estimation of static and dynamic brain graphs. Path‐based analysis identifies disrupted connectors and disconnectors in schizophrenia, revealing network alterations undetectable by unimodal or pairwise approaches and highlighting potential biomarkers of ...
Falakshahi H, Rokham H, Mathalon DH, Ford JM, Mueller BA, Preda A, van Erp TGM, Turner JA, Pearlson G, Calhoun VD.   +9 more
europepmc   +2 more sources

Diffusion Inverse Filtering: Enhancing Functional Connectivity-Based Pattern Recognition by Counteracting Spatial Smoothing [PDF]

Brain Sciences
Background/Objectives: This study proposes Diffusion Inverse Filtering (DIF), a spatially informed transformation designed to counteract spatial smoothing in functional-connectivity representations (i.e., functional networks) and thereby enhance their ...
Yuzeng Xu, Sho Otsuka, Seiji Nakagawa
doaj   +2 more sources

ERG-Graph: Graph Signal Processing of the Electroretinogram for Classification of Neurodevelopmental Disorders [PDF]

Bioengineering
Objective biomarkers for neurodevelopmental disorders remain an unmet clinical need. The electroretinogram (ERG), a non-invasive recording of the retinal response to light, has shown promise as a physiological marker for autism spectrum disorder (ASD ...
Luis Roberto Mercado-Diaz, Javier O. Pinzon-Arenas, Paul A. Constable, Irene O. Lee, Lynne Loh, Dorothy A. Thompson, Hugo F. Posada-Quintero   +6 more
doaj   +2 more sources

STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling. [PDF]

Adv Sci (Weinh)
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Liu J, Sun S, Lv Z, Liu X, Wang Y, Liu B.   +5 more
europepmc   +2 more sources

A Dimensionality Reduction Approach for Motor Imagery Brain–Computer Interface Using Functional Clustering and Graph Signal Processing [PDF]

Journal of Medical Signals and Sensors
Background: This paper introduces an approach for dimensionality reduction and classification of electroencephalogram signals in motor imagery brain–computer interface (MI-BCI) systems.
Mohammad Davood Khalili, Vahid Abootalebi, Hamid Saeedi-Sourck   +2 more
doaj   +2 more sources

Graphs Constructed from Instantaneous Amplitude and Phase of Electroencephalogram Successfully Differentiate Motor Imagery Tasks [PDF]

Journal of Medical Signals and Sensors
Background: Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations.
Maliheh Miri, Vahid Abootalebi, Hamid Saeedi-Sourck, Dimitri Van De Ville, Hamid Behjat   +4 more
doaj   +2 more sources