AIMC Topic: Imagination

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Advancing BCI with a transformer-based model for motor imagery classification.

Scientific reports Jul 2, 2025
Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering significant benefits for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI)...
Humans Deep Learning Electroencephalography Brain-Computer Interfaces Imagination Neural Networks, Computer Machine Learning Algorithms
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Towards decoding motor imagery from EEG signal using optimized back propagation neural network with honey badger algorithm.

Scientific reports Jul 1, 2025
The importance of using Brain-Computer Interface (BCI) systems based on electro encephalography (EEG) signal to decode Motor Imagery(MI) is very impressive because of the possibility of analyzing and translating brain signals related to movement inte...
Movement Imagination Electroencephalography Humans Signal Processing, Computer-Assisted Brain-Computer Interfaces Neural Networks, Computer Algorithms Brain
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Speech imagery brain-computer interfaces: a systematic literature review.

Journal of neural engineering Jun 26, 2025
Speech Imagery (SI) refers to the mental experience of hearing speech and may be the core of verbal thinking for people who undergo internal monologues. It belongs to the set of possible mental imagery states that produce kinesthetic experiences whos...
Brain-Computer Interfaces Imagination Brain Humans Speech Electroencephalography
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POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.

Journal of neural engineering Jun 11, 2025
. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal wa...
Machine Learning Adult Male Electroencephalography Humans Neural Networks, Computer Brain-Computer Interfaces Imagination
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Unsupervised Domain Adaptation With Synchronized Self-Training for Cross- Domain Motor Imagery Recognition.

IEEE journal of biomedical and health informatics May 6, 2025
Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to ...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination Humans Male Algorithms Unsupervised Machine Learning Electroencephalography Female Young Adult Adult
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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...
Brain-Computer Interfaces Signal-To-Noise Ratio Movement Deep Learning Electroencephalography Signal Processing, Computer-Assisted Imagination Humans Neural Networks, Computer Algorithms
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Enhanced Brain Functional Interaction Following BCI-Guided Supernumerary Robotic Finger Training Based on Sixth-Finger Motor Imagery.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Apr 29, 2025
Supernumerary robotic finger (SRF) has shown unique advantages in the field of motor augmentation and rehabilitation, while the development of brain computer interface (BCI) technology has provided the possibility for direct control of SRF. However, ...
Magnetic Resonance Imaging Fingers Neuronal Plasticity Motor Cortex Brain Mapping Young Adult Humans Imagination Robotics Adult Brain Psychomotor Performance Female Male Brain-Computer Interfaces
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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 ...
Imagination Brain-Computer Interfaces Signal-To-Noise Ratio Movement Humans Electroencephalography Neural Networks, Computer Adult Signal Processing, Computer-Assisted Algorithms Male Attention Deep Learning
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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...
Humans Deep Learning Electroencephalography Signal Processing, Computer-Assisted Intention Imagination Neural Networks, Computer Young Adult Brain Adult Female Male
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Multi-scale convolutional transformer network for motor imagery brain-computer interface.

Scientific reports Apr 15, 2025
Brain-computer interface (BCI) systems allow users to communicate with external devices by translating neural signals into real-time commands. Convolutional neural networks (CNNs) have been effectively utilized for decoding motor imagery electroencep...
Brain Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination Humans Neural Networks, Computer
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