AIMC Topic: Imagination

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Multiband Convolutional Riemannian Network With Band-Wise Riemannian Triplet Loss for Motor Imagery Classification.

IEEE journal of biomedical and health informatics Dec 5, 2024
This paper presents a novel motor imagery classification algorithm that uses an overlapping multiscale multiband convolutional Riemannian network with band-wise Riemannian triplet loss to improve classification performance. Despite the superior perfo...
Humans Neural Networks, Computer Algorithms Deep Learning Signal Processing, Computer-Assisted Electroencephalography Brain-Computer Interfaces Imagination
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Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis.

IEEE transactions on neural networks and learning systems Dec 2, 2024
The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces (BCIs) based on electroencephalography (EEG). Over the past few decades, the performance of MI-EEG classifiers has seen gradual enhancement. In thi...
Graph Neural Networks Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Imagination Neural Networks, Computer Deep Learning Algorithms Time Factors Electroencephalography
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A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training.

IEEE transactions on neural networks and learning systems Dec 2, 2024
Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as mot...
Humans Neural Networks, Computer Electroencephalography Brain-Computer Interfaces Imagination Machine Learning Algorithms Brain
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An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.

Medical & biological engineering & computing Nov 29, 2024
Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and sel...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination Machine Learning Algorithms Electroencephalography Humans
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MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.

Sensors (Basel, Switzerland) Nov 28, 2024
Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (L...
Humans Neural Networks, Computer Algorithms Imagination Signal Processing, Computer-Assisted Brain Attention Lower Extremity Brain-Computer Interfaces Electroencephalography
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FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.

PloS one Nov 21, 2024
Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (D...
Imagination Deep Learning Electroencephalography Humans Brain-Computer Interfaces Neural Networks, Computer
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Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning based motor imagery classification for brain computer interface.

PloS one Nov 21, 2024
Motor imagery (MI) classification has been commonly employed in making brain-computer interfaces (BCI) to manage the outside tools as a substitute neural muscular path. Effectual MI classification in BCI improves communication and mobility for people...
Humans Electroencephalography Brain Algorithms Brain-Computer Interfaces Imagination Deep Learning Support Vector Machine
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Enhancing Motor Imagery Classification with Residual Graph Convolutional Networks and Multi-Feature Fusion.

International journal of neural systems Nov 19, 2024
Stroke, an abrupt cerebrovascular ailment resulting in brain tissue damage, has prompted the adoption of motor imagery (MI)-based brain-computer interface (BCI) systems in stroke rehabilitation. However, analyzing electroencephalogram (EEG) signals f...
Humans Neural Networks, Computer Brain Signal Processing, Computer-Assisted Stroke Male Female Stroke Rehabilitation Brain-Computer Interfaces Aged Middle Aged Imagination Adult Electroencephalography
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A protocol for trustworthy EEG decoding with neural networks.

Neural networks : the official journal of the International Neural Network Society Nov 2, 2024
Deep learning solutions have rapidly emerged for EEG decoding, achieving state-of-the-art performance on a variety of decoding tasks. Despite their high performance, existing solutions do not fully address the challenge posed by the introduction of m...
Deep Learning Electroencephalography Brain-Computer Interfaces Event-Related Potentials, P300 Imagination Brain Adult Male Neural Networks, Computer Algorithms Humans
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Dual Attention Relation Network With Fine-Tuning for Few-Shot EEG Motor Imagery Classification.

IEEE transactions on neural networks and learning systems Oct 29, 2024
Recently, motor imagery (MI) electroencephalography (EEG) classification techniques using deep learning have shown improved performance over conventional techniques. However, improving the classification accuracy on unseen subjects is still challengi...
Electroencephalography Signal Processing, Computer-Assisted Attention Neural Networks, Computer Algorithms Deep Learning Brain-Computer Interfaces Signal-To-Noise Ratio Imagination Humans
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