Cited by CrCo- Mlgcn: A Cross-scale Co-learning Based Multi-Level Graph Convolutional Network for Brain-Computer Interface

Evolving brain network dynamics in early childhood: Insights from modular graph metrics DOI

Zeyu Song, Zhenqi Jiang, Zhao Zhang

et al.

NeuroImage, Journal Year: 2024, Volume and Issue: 297, P. 120740 - 120740

Published: July 23, 2024

Modular dynamic graph theory metrics effectively capture the patterns of information interaction during human brain development. While existing research has employed modular algorithms to examine overall impact changes in community structure throughout development, there is a notable gap understanding cross-community within different functional networks early childhood and their potential contributions efficiency transmission. This study seeks address this by tracing trajectories structural modeling transmission efficiency. We analyzed 194 imaging scans from 83 children aged 2 8 years, who participated passive viewing magnetic resonance sessions. Utilizing sliding windows algorithms, we evaluated three spatiotemporal metrics-temporal flexibility, diversity, within-community diversity-and four centrality metrics: degree centrality, eigenvector between-community centrality. Mixed-effects linear models revealed significant age-related increases temporal flexibility default mode network (DMN), executive control (ECN), salience (SN), indicating frequent adjustments these childhood. Additionally, diversity SN also displayed increases, highlighting its broad pattern interactions. Conversely, language exhibited decreases, reflecting network's gradual specialization. Furthermore, our findings indicated across DMN, ECN, SN, network, dorsal attention while increased significantly for SN. However, remained stable all These results suggest that interactions communities remains stable. Finally, mediation analysis was conducted explore relationships between age, metrics, both global local based on structure. The primarily mediated relationship age decrease efficiency, those increase suggests developmental trajectory integration segregation, with playing pivotal role transformation. provides novel insights into mechanisms which development impacts through networks.

Language: Английский

Citations

Classification Algorithm for fNIRS-based Brain Signals Using Convolutional Neural Network with Spatiotemporal Feature Extraction Mechanism DOI

Yuxin Qin, Baojiang Li, Wenlong Wang

et al.

Neuroscience, Journal Year: 2024, Volume and Issue: 542, P. 59 - 68

Published: Feb. 17, 2024

Language: Английский

Citations

Enhanced Sentiment Analysis and Topic Modeling during the Pandemic using Automated Latent Dirichlet Allocation DOI

Amreen Batool,

Yung-Cheol Byun

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 81206 - 81220

Published: Jan. 1, 2024

Language: Английский

Citations

EEG–fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network DOI

Guijun Chen, Yue Liu, Xueying Zhang

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(8), P. 820 - 820

Published: Aug. 16, 2024

Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person’s emotional state have been widely studied in emotion recognition. However, the effective feature fusion discriminative learning from EEG–fNIRS data is challenging. In order to improve accuracy of recognition, graph convolution capsule attention network model (GCN-CA-CapsNet) proposed. Firstly, signals are collected 50 subjects induced by video clips. And then, features EEG fNIRS extracted; fused generate higher-quality primary capsules with Pearson correlation adjacency matrix. Finally, module introduced assign different weights capsules, selected better classification dynamic routing mechanism. We validate efficacy proposed method on our dataset an ablation study. Extensive experiments demonstrate that GCN-CA-CapsNet achieves more satisfactory performance against state-of-the-art methods, average increase 3–11%.

Language: Английский

Citations

FCS-TPNet: Fusion of fNIRS chromophore signals to construct temporal-spatial graph representation for topological networks DOI

Lin F. Yang, Jiang Gu, Jun Chen

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 104, P. 107528 - 107528

Published: Jan. 27, 2025

Language: Английский

Citations

Brain functional connectivity analysis of fMRI-based Alzheimer's disease data DOI

Maitha Alarjani, Badar Almarri

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: Feb. 19, 2025

The prevalence of Alzheimer's disease (AD) poses a significant public health challenge. Distinguishing AD stages remains complex process due to ambiguous variability within and across stages. Manual classification such multifaceted massive data brain volumes is operationally inefficient vulnerable human errors. Here, we propose precise systematic framework for classification. core this discovers analyzes functional connectivity among regions interest (ROIs) brain. Multivariate Pattern Analysis (MVPA) applied extract features that reveal patterns in the These are then used as inputs an Extreme Learning Machine (ELM) model classify model's performance assessed through comprehensive evaluation metrics ensure robustness reliability. Applying on datasets which contain meticulously validated fMRI scans OASIS Neuroimaging Initiative datasets, validate merit proposed work. framework's results show improvement collective two-class multi-class Feeding ELM with MVPA yield decent outcomes given generalizable computationally-efficient model. This study underscores effectiveness approach accurately distinguishing stages, offering potential improvements detection.

Language: Английский

Citations

Multimodal Machine Learning Analysis of fNIRS Signals Using LSTM and KNN Models for Cognitive States and Brain Activation Patterns Prediction DOI

Adrian Luckiewicz, Dariusz Mikołajewski, Radosław Roszczyk

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 275 - 288

Published: Jan. 1, 2025

Language: Английский

Citations

The relationship between coal miners’ Big Five personality traits and risk propensity: Evidence from fNIRS DOI

Junrui Mao, Shuicheng Tian, Fangyuan Tian

et al.

International Journal of Industrial Ergonomics, Journal Year: 2025, Volume and Issue: 107, P. 103750 - 103750

Published: April 28, 2025

Language: Английский

Citations

Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD DOI

Yucun Zhong, Lin Yao, Gang Pan

et al.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2024, Volume and Issue: 32, P. 662 - 671

Published: Jan. 1, 2024

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement control resulting a lower signal-to-noise ratio. The quality of collected data significantly impacts cross-subject calibration results. To address this challenge, we introduce novel method passive tactile afferent stimulation, which induced by stimulation utilized to calibrate transfer learning models for decoding. During experiments, was applied either left or right hand, with subjects only required sense stimulation. Data from these tasks were used train fine-tune subsequently decode pure data. We evaluated BCI performance using both classical Common Spatial Pattern (CSP) combined Linear Discriminant Analysis (LDA) algorithm state-of-the-art deep model. results demonstrate that proposed achieved decoding at an equivalent level traditional calibration, added benefit outperforming fewer trials. simplicity effectiveness make it valuable practical applications BCI, especially clinical settings.

Language: Английский

Citations

Remote Cardiac System Monitoring Using 6G-IoT Communication and Deep Learning DOI

Abdulbasid S. Banga, Mohammed M. Alenazi, Nisreen Innab

et al.

Wireless Personal Communications, Journal Year: 2024, Volume and Issue: 136(1), P. 123 - 142

Published: May 1, 2024

Language: Английский

Citations