AIMC Topic: Imagination

Clear Filters Showing 61 to 70 of 205 articles

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.

Neural networks : the official journal of the International Neural Network Society Jul 1, 2024
The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based...
Humans Neural Networks, Computer Algorithms Supervised Machine Learning Brain-Computer Interfaces Imagination Unsupervised Machine Learning Electroencephalography Brain
View on PubMed DOI

STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.

Neural networks : the official journal of the International Neural Network Society Jun 26, 2024
Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end...
Movement Imagination Brain-Computer Interfaces Humans Brain Neural Networks, Computer Electroencephalography
View on PubMed DOI

Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention.

Neural networks : the official journal of the International Neural Network Society Jun 17, 2024
Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification per...
Electroencephalography Brain-Computer Interfaces Imagination Male Deep Learning Time Factors Neural Networks, Computer Algorithms Machine Learning Adult Humans
View on PubMed DOI

An end-to-end multi-task motor imagery EEG classification neural network based on dynamic fusion of spectral-temporal features.

Computers in biology and medicine Jun 8, 2024
Electroencephalograph (EEG) brain-computer interfaces (BCI) have potential to provide new paradigms for controlling computers and devices. The accuracy of brain pattern classification in EEG BCI is directly affected by the quality of features extract...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination Brain Electroencephalography Humans Neural Networks, Computer
View on PubMed DOI

Optimizing motor imagery BCI models with hard trials removal and model refinement.

Biomedical physics & engineering express Jun 4, 2024
Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this p...
Algorithms Deep Learning Electroencephalography Neural Networks, Computer Artificial Intelligence Humans Brain-Computer Interfaces Imagination
View on PubMed DOI

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.

IEEE transactions on neural networks and learning systems Jun 3, 2024
Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the ...
Brain-Computer Interfaces Imagination Brain Adult Humans Neural Networks, Computer Deep Learning Algorithms Electroencephalography Signal Processing, Computer-Assisted
View on PubMed DOI

A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces.

Computers in biology and medicine May 20, 2024
In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spect...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Brain Imagination Humans Support Vector Machine Algorithms Electroencephalography
View on PubMed DOI

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network.

Journal of neural engineering May 17, 2024
Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samp...
Imagination Pattern Recognition, Automated Brain-Computer Interfaces Neural Networks, Computer Electroencephalography Humans Supervised Machine Learning
View on PubMed DOI

Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks.

Sensors (Basel, Switzerland) May 16, 2024
The widely adopted paradigm in brain-computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, wh...
Signal Processing, Computer-Assisted Attention Brain-Computer Interfaces Algorithms Electroencephalography Humans Imagination Neural Networks, Computer
View on PubMed DOI

Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.

Medical & biological engineering & computing May 9, 2024
Motor imagery (MI) based brain-computer interfaces (BCIs) decode the users' intentions from electroencephalography (EEG) to achieve information control and interaction between the brain and external devices. In this paper, firstly, we apply Riemannia...
Brain Support Vector Machine Signal Processing, Computer-Assisted Brain-Computer Interfaces Electroencephalography Imagination Humans Algorithms
View on PubMed DOI
Popular Topics
Recent Journals