A bidirectional cross-modal transformer representation learning model for EEG-fNIRS multimodal affective BCI DOI
Xiaopeng Si, Shuai Zhang, Zhuobin Yang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 266, P. 126081 - 126081

Published: Dec. 10, 2024

Language: Английский

Development of an Integrated System of sEMG Signal Acquisition, Processing, and Analysis with AI Techniques DOI Creative Commons
Filippo Laganá, Danilo Pratticò, Giovanni Angiulli

et al.

Signals, Journal Year: 2024, Volume and Issue: 5(3), P. 476 - 493

Published: July 26, 2024

The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis surface electromyographic (sEMG) signals. analyzes sEMG signals to understand muscle function neuromuscular control, employing convolutional neural networks (CNNs) pattern recognition. electrical analyzed on healthy unhealthy subjects are acquired using meticulously developed featuring biopotential acquisition electrodes. captured database extracted, classified, interpreted by application CNNs with aim identifying patterns indicative problems. By leveraging advanced learning techniques, proposed method addresses non-stationary nature recordings mitigates cross-talk effects commonly observed interference sensors. integration AI algorithm signal enhances qualitative outcomes eliminating redundant information. reveals their effectiveness accurately deciphering complex data from signals, problems high precision. paper contributes landscape biomedical research, advocating computational techniques unravel physiological phenomena enhance utility analysis.

Language: Английский

Citations

13
Fusion analysis of EEG-fNIRS multimodal brain signals: a multitask classification algorithm incorporating spatial-temporal convolution and dual attention mechanisms DOI
Xingbin Shi, Haiyan Wang, Baojiang Li

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2025, Volume and Issue: 74, P. 1 - 12

Published: Jan. 1, 2025

Language: Английский

Citations

0
STA-Net: Spatial–temporal alignment network for hybrid EEG-fNIRS decoding DOI
Mutian Liu, Banghua Yang, Lin Meng

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103023 - 103023

Published: Feb. 1, 2025

Language: Английский

Citations

0
A review of hybrid EEG-based multimodal human–computer interfaces using deep learning: applications, advances, and challenges DOI
Hyung-Tak Lee, Miseon Shim,

Xianghong Liu

et al.

Biomedical Engineering Letters, Journal Year: 2025, Volume and Issue: unknown

Published: March 22, 2025

Language: Английский

Citations

0
Decouple and Rebalance: Towards Effective Joint Learning for EEG-fNIRS Integration DOI
Ming Meng,

Deshui Hao,

Yunyuan Gao

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130128 - 130128

Published: March 1, 2025

Language: Английский

Citations

0
A Review of Machine Learning-Based Assessment of Depression DOI
Zhao Wang, Ziyi Cai,

Shuya Dong

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 266 - 290

Published: Jan. 1, 2025

Language: Английский

Citations

0
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

0
TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals DOI
Türker Tuncer, İrem Taşçı, Burak Taşçı

et al.

Applied Acoustics, Journal Year: 2024, Volume and Issue: 228, P. 110307 - 110307

Published: Sept. 27, 2024

Language: Английский

Citations

2
Temporal attention fusion network with custom loss function for EEG--fNIRS classification DOI
Chayut Bunterngchit, Jiaxing Wang, Jianqiang Su

et al.

Journal of Neural Engineering, Journal Year: 2024, Volume and Issue: 21(6), P. 066016 - 066016

Published: Nov. 4, 2024

Abstract Objective. Methods that can detect brain activities accurately are crucial owing to the increasing prevalence of neurological disorders. In this context, a combination electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offers powerful approach understanding normal pathological functions, thereby overcoming limitations each modality, such as susceptibility artifacts EEG limited temporal resolution fNIRS. However, challenges class imbalance inter-class variability within multisubject data hinder their full potential. Approach. To address issue, we propose novel attention fusion network (TAFN) with custom loss function. The TAFN model incorporates mechanisms its long short-term memory convolutional layers capture spatial dependencies in EEG–fNIRS data. function combines weights asymmetric terms ensure precise classification cognitive motor intentions, along addressing issues. Main results. Rigorous testing demonstrated exceptional cross-subject accuracy TAFN, exceeding 99% for tasks 97% imagery (MI) tasks. Additionally, ability subtle differences epilepsy was analyzed using scalp topography MI Significance. This study presents technique outperforms traditional methods detecting high-precision activity associated patterns. makes it promising tool applications seizure detection, which discerning pattern is paramount importance.

Language: Английский

Citations

0
A bidirectional cross-modal transformer representation learning model for EEG-fNIRS multimodal affective BCI DOI
Xiaopeng Si, Shuai Zhang, Zhuobin Yang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 266, P. 126081 - 126081

Published: Dec. 10, 2024

Language: Английский

Citations

0