Multi-scale attention mechanism residual neural network for fault diagnosis of rolling bearings

Y Wang, J Liang, X Gu, D Ling… - Proceedings of the …, 2022 - journals.sagepub.com
Rolling bearing fault diagnosis is crucial to improve industrial safety and reliability. In recent
years, intelligent fault diagnosis method represented by deep learning (DL) has been …

Energy-efficient dynamic channel allocation algorithm in wireless body area network

M Ashraf, S Hassan, S Rubab, MA Khan… - Environment …, 2022 - Springer
Abstract Wireless Body Area Networks (WBAN) is an emerging technology aimed at to
support and provide real-time health monitoring and timely curing patient's life under any life …

HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals

R Bhadra, PK Singh, M Mahmud - Brain Informatics, 2024 - Springer
Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES
detection with high accuracy from electroencephalogram (EEG) signals. The early detection …

Leveraging Temporal Dependency for Cross-subject-MI BCIs by Contrastive Learning and Self-attention

H Sun, Y Ding, J Bao, K Qin, C Tong, J Jin, C Guan - Neural Networks, 2024 - Elsevier
Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found
extensive utilization in motor rehabilitation and the control of assistive applications …

Analysis of epileptic iEEG data by applying convolutional neural networks to low-frequency scalograms

M Bayram, MA Arserim - IEEE Access, 2021 - ieeexplore.ieee.org
In this paper, Convolutional Neural Networks (CNN) method was applied to low frequency
scalograms in order to contribute to the development of diagnostic and early diagnosis …

A multi‐feature fusion graph attention network for decoding motor imagery intention in spinal cord injury patients

J Leng, L Gao, X Jiang, Y Lou, Y Sun… - Journal of Neural …, 2024 - iopscience.iop.org
Electroencephalogram (EEG) signals exhibit multi-domain features, and electrode
distributions follow non-Euclidean topology. To fully resolve the EEG signals, this study …

Characteristic analysis of epileptic brain network based on attention mechanism

HS Yu, XF Meng - Scientific Reports, 2023 - nature.com
Constructing an efficient and accurate epilepsy detection system is an urgent research task.
In this paper, we developed an EEG-based multi-frequency multilayer brain network …

Fusing CNNs and attention-mechanisms to improve real-time indoor Human Activity Recognition for classifying home-based physical rehabilitation exercises

M Zaher, AS Ghoneim, L Abdelhamid, A Atia - Computers in Biology and …, 2025 - Elsevier
Physical rehabilitation plays a critical role in enhancing health outcomes globally. However,
the shortage of physiotherapists, particularly in developing countries where the ratio is …

Epilepsy Detection by Different Modalities with the Use of AI-Assisted Models

J Vajiram, S Sivakumar, R Jena… - Artificial Intelligence …, 2024 - ojs.bonviewpress.com
Epilepsy is characterized by recurrent seizures originating from any four brain lobes. It
includes focal seizures with symptoms of alterations in consciousness and cognitive …

Space-CNN: a decision classification method based on EEG signals from different brain regions

H Xue, J Yang, W Zhang, B Yang - Medical & Biological Engineering & …, 2024 - Springer
Decision-making plays a critical role in an individual's interpersonal interactions and
cognitive processes. Due to the issue of strong subjectivity in the classification research of …