AIMC Topic: Signal Processing, Computer-Assisted

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A Novel 3D Approach with a CNN and Swin Transformer for Decoding EEG-Based Motor Imagery Classification.

Sensors (Basel, Switzerland) May 5, 2025
Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to control external devices and achieve movement functions by simply imagining bodily motions. Despite t...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Signal-To-Noise Ratio Movement Imagination Algorithms Deep Learning Electroencephalography Humans Neural Networks, Computer
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Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification.

IEEE transactions on neural networks and learning systems May 2, 2025
The brain signal classification is the basis for the implementation of brain-computer interfaces (BCIs). However, most existing brain signal classification methods are based on signal processing technology, which require a significant amount of manua...
Brain-Computer Interfaces Humans Electroencephalography Algorithms Neural Networks, Computer Signal Processing, Computer-Assisted Brain
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Balancing privacy and health integrity: A novel framework for ECG signal analysis in immersive environments.

Computers in biology and medicine May 1, 2025
The widespread use of immersive technologies such as Virtual Reality, Mixed Reality, and Augmented Reality has led to the continuous collection and streaming of vast amounts of sensitive biometric data. Among the biometric signals collected, ECG (ele...
Electrocardiography Signal Processing, Computer-Assisted Confidentiality Privacy Humans Algorithms
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TasteNet: A novel deep learning approach for EEG-based basic taste perception recognition using CEEMDAN domain entropy features.

Journal of neuroscience methods Apr 30, 2025
BACKGROUND: Taste perception is the process by which the gustatory system detects and interprets chemical stimuli from food and beverages, involving activation of taste receptors on the tongue. Analyzing taste perception is essential for understandin...
Male Deep Learning Electroencephalography Signal Processing, Computer-Assisted Female Humans Taste Perception Young Adult Neural Networks, Computer Entropy Adult Brain
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SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 29, 2025
Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due to individual discrepancy among subjects and low signal-to-noise ratio of EEG signals. We propose an ...
Electroencephalography Imagination Adult Humans Brain-Computer Interfaces Signal Processing, Computer-Assisted Neural Networks, Computer Male Signal-To-Noise Ratio Attention Algorithms Deep Learning Movement
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Enhanced EEG-based Alzheimer's disease detection using synchrosqueezing transform and deep transfer learning.

Neuroscience Apr 24, 2025
The most prevalent type of dementia and a progressive neurodegenerative disease, Alzheimer's disease has a major influence on day-to-day functioning due to memory loss, cognitive decline, and behavioral problems. By using synchrosqueezing representat...
Deep Learning Electroencephalography Signal Processing, Computer-Assisted Neural Networks, Computer Brain Male Alzheimer Disease Humans
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Data-driven sleep structure deciphering based on cardiorespiratory signals.

Computer methods and programs in biomedicine Apr 23, 2025
BACKGROUND AND OBJECTIVE: Cardiorespiratory signals provide a novel perspective for understanding sleep structure through the physiological mechanism of cardiopulmonary coupling. This mechanism divides the coupling spectrum into high-frequency (HF) a...
Adult Humans Sleep Electrooculography Deep Learning Algorithms Sleep Stages Male Electroencephalography Polysomnography Female Middle Aged Signal Processing, Computer-Assisted
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AdamGraph: Adaptive Attention-Modulated Graph Network for EEG Emotion Recognition.

IEEE transactions on cybernetics Apr 23, 2025
The underlying time-variant and subject-specific brain dynamics lead to inconsistent distributions in electroencephalogram (EEG) topology and representations within and between individuals. However, current works primarily align the distributions of ...
Algorithms Databases, Factual Attention Brain Humans Emotions Neural Networks, Computer Electroencephalography Signal Processing, Computer-Assisted
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Dynamic Hierarchical Convolutional Attention Network for Recognizing Motor Imagery Intention.

IEEE transactions on cybernetics Apr 23, 2025
The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) decoding models, especially those based on deep learning, predominantly focus on global spatial feature...
Signal Processing, Computer-Assisted Intention Imagination Male Deep Learning Humans Electroencephalography Neural Networks, Computer Female Brain Young Adult Adult
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HATNet: EEG-Based Hybrid Attention Transfer Learning Network for Train Driver State Detection.

IEEE transactions on cybernetics Apr 23, 2025
Electroencephalography (EEG) is widely utilized for train driver state detection due to its high accuracy and low latency. However, existing methods for driver status detection rarely use the rich physiological information in EEG to improve detection...
Humans Automobile Driving Neural Networks, Computer Electroencephalography Machine Learning Algorithms Signal Processing, Computer-Assisted Adult Attention Male
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