AIMC Topic: Imagination

Clear Filters Showing 41 to 50 of 205 articles

Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.

Computers in biology and medicine Oct 18, 2024
In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered significant attention due to its extensive applicability in healthcare, including areas such as assistive robotics and rehabilitation engineering. Neverthele...
Humans Neural Networks, Computer Signal Processing, Computer-Assisted Attention Brain-Computer Interfaces Imagination Brain Deep Learning Electroencephalography
View on PubMed DOI

Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Oct 1, 2024
Motor imagery refers to the brain's response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio (SNR) due to various artifact...
Imagination Signal Processing, Computer-Assisted Artifacts Signal-To-Noise Ratio Humans Neural Networks, Computer Algorithms Deep Learning Electroencephalography
View on PubMed DOI

Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 27, 2024
Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized features and requires a more complex ...
Male Deep Learning Electroencephalography Brain-Computer Interfaces Cerebral Cortex Female Humans Imagination Young Adult Neural Networks, Computer Adult Algorithms
View on PubMed DOI

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 18, 2024
Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this ch...
Deep Learning Electroencephalography Privacy Humans Machine Learning Brain-Computer Interfaces Algorithms Computer Security Imagination
View on PubMed DOI

Automatic Feature Selection for Sensorimotor Rhythms Brain-Computer Interface Fusing Expert and Data-Driven Knowledge.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 18, 2024
Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods. Despite the advances i...
Electroencephalography Brain-Computer Interfaces Imagination Humans Reproducibility of Results Machine Learning Adult Algorithms Fuzzy Logic
View on PubMed DOI

Decoding Multi-Class Motor Imagery From Unilateral Limbs Using EEG Signals.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 17, 2024
The EEG is a widely utilized neural signal source, particularly in motor imagery-based brain-computer interface (MI-BCI), offering distinct advantages in applications like stroke rehabilitation. Current research predominantly concentrates on the bila...
Humans Machine Learning Algorithms Female Young Adult Stroke Rehabilitation Adult Imagination Male Brain-Computer Interfaces Electroencephalography Movement Healthy Volunteers Upper Extremity
View on PubMed DOI

A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Sep 16, 2024
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredi...
Imagination Electroencephalography Brain-Computer Interfaces Movement Adult Male Deep Learning Humans Algorithms Female Neural Networks, Computer Machine Learning
View on PubMed DOI

A Spatio-Temporal Capsule Neural Network with Self-Correlation Routing for EEG Decoding of Semantic Concepts of Imagination and Perception Tasks.

Sensors (Basel, Switzerland) Sep 15, 2024
Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasiv...
Imagination Semantics Brain-Computer Interfaces Perception Humans Neural Networks, Computer Algorithms Electroencephalography Signal Processing, Computer-Assisted
View on PubMed DOI

CTNet: a convolutional transformer network for EEG-based motor imagery classification.

Scientific reports Aug 30, 2024
Brain-computer interface (BCI) technology bridges the direct communication between the brain and machines, unlocking new possibilities for human interaction and rehabilitation. EEG-based motor imagery (MI) plays a pivotal role in BCI, enabling the tr...
Humans Imagination Neural Networks, Computer Algorithms Electroencephalography Brain Signal Processing, Computer-Assisted Brain-Computer Interfaces
View on PubMed DOI

Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks.

Neural networks : the official journal of the International Neural Network Society Aug 28, 2024
In brain-computer interface (BCI), building accurate electroencephalogram (EEG) classifiers for specific mental tasks is critical for BCI performance. The classifiers are developed by machine learning (ML) and deep learning (DL) techniques, requiring...
Humans Neural Networks, Computer Electroencephalography Brain-Computer Interfaces Imagination Machine Learning Brain Deep Learning
View on PubMed DOI
Popular Topics
Recent Journals