Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment

Sensors, Journal Year: 2025, Volume and Issue: 25(3), P. 706 - 706

Published: Jan. 24, 2025

This study focuses on the diagnostic analysis of cartilage damage in knee joint based acoustic signals generated by joint. The research utilizes a combination advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation (DFA), alongside convolutional neural networks (CNNs) for classification detection tasks. Acoustic signals, often reflecting mechanical behavior during movement, serve as non-invasive tool assessing condition. EEMD is applied to decompose into intrinsic functions (IMFs), which are then analyzed using DFA quantify scaling properties detect irregularities indicative damage. separation individual frequency components allows multi-scale with each resulting from local variations amplitude over time allowing effective removal noise present signal. CNN model trained features extracted these accurately classify different stages degeneration. proposed method demonstrates potential early pathology, providing valuable preventive healthcare reducing need invasive procedures. results suggest that EEMD-DFA feature extraction offers promising approach assessment

Language: Английский

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

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

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

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

Published: Jan. 1, 2025

Language: Английский

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Emotion Recognition Based on a EEG–fNIRS Hybrid Brain Network in the Source Space

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1166 - 1166

Published: Nov. 22, 2024

Background/Objectives: Studies have shown that emotion recognition based on electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) multimodal physiological signals exhibits superior performance compared to of unimodal approaches. Nonetheless, there remains a paucity in-depth investigations analyzing the inherent relationship between EEG fNIRS constructing brain networks improve recognition. Methods: In this study, we introduce an innovative method construct hybrid in source space simultaneous EEG-fNIRS for Specifically, perform localization derive signals. Subsequently, causal are established by Granger causality signals, while coupled formed assessing coupling strength The resultant integrated create space, which serve as features Results: effectiveness our proposed is validated multiple datasets. experimental results indicate approach significantly surpasses baseline method. Conclusions: This work offers novel perspective fusion emotion-evoked paradigm provides feasible solution enhancing performance.

Language: Английский

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Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system

Frontiers in Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: May 8, 2024

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development application this novel fNIRS system, emphasizing its potential revolutionize management providing real-time solution. Fifteen healthy university students participated controlled study, where monitored their PFC activity blood under fasting glucose-loaded conditions. findings reveal significant correlation between activity, as measured our levels, suggesting feasibility technology CGM. nature overcomes mobility limitations traditional setups, enabling continuous, everyday settings. We identified 10 critical features related from extensive data successfully correlated function with constructing predictive models. Results show positive association exhibiting clear response glucose. Furthermore, improved regressive rule principal component analysis (PCA) method outperforms PCA model prediction. propose validation approach based on leave-one-out cross-validation, demonstrating unique advantages K-nearest neighbor (KNN) Comparative existing CGM methods reveals paper's KNN exhibits lower RMSE MARD at 0.11 8.96%, respectively, were highly actual (r = 0.995, p < 0.000). This provides valuable insights into relationship metabolic states brain laying foundation innovative solutions. represents advancement effective management, offering promising alternative current paving way future health personalized medicine.

Language: Английский

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Correction: Chen et al. Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System. Electronics 2024, 13, 1310

Electronics, Journal Year: 2024, Volume and Issue: 13(10), P. 1833 - 1833

Published: May 9, 2024

In the original publication [...]

Language: Английский

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Online Multi-level Contrastive Representation Distillation for Cross-Subject fNIRS Emotion Recognition

Published: Oct. 20, 2024

Language: Английский

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