AIMC Topic: Brain-Computer Interfaces

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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 Deep Learning Electroencephalography Signal Processing, Computer-Assisted Neural Networks, Computer Algorithms Brain-Computer Interfaces Imagination
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Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques.

Scientific reports Dec 4, 2024
In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim...
Heuristics Brain-Computer Interfaces Electroencephalography Signal Processing, Computer-Assisted Neural Networks, Computer Algorithms Deep Learning Emotions Male Humans
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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...
Imagination Graph Neural Networks Deep Learning Time Factors Signal Processing, Computer-Assisted Brain-Computer Interfaces Electroencephalography Humans Neural Networks, Computer Algorithms
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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 Machine Learning Electroencephalography Brain-Computer Interfaces Imagination 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...
Humans Machine Learning Electroencephalography Signal Processing, Computer-Assisted Algorithms Brain-Computer Interfaces Imagination
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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...
Attention Brain-Computer Interfaces Imagination Electroencephalography Signal Processing, Computer-Assisted Algorithms Brain Humans Lower Extremity Neural Networks, Computer
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Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.

Journal of neuroscience methods Nov 23, 2024
Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this pape...
Brain Electroencephalography Brain-Computer Interfaces Signal Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms
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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...
Brain-Computer Interfaces Imagination Deep Learning Humans Neural Networks, Computer Electroencephalography
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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...
Electroencephalography Brain-Computer Interfaces Algorithms Deep Learning Humans Support Vector Machine Brain Imagination
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A continuous pursuit dataset for online deep learning-based EEG brain-computer interface.

Scientific data Nov 20, 2024
This dataset is from an EEG brain-computer interface (BCI) study investigating the use of deep learning (DL) for online continuous pursuit (CP) BCI. In this task, subjects use Motor Imagery (MI) to control a cursor to follow a randomly moving target,...
Electroencephalography Brain-Computer Interfaces Humans Algorithms Deep Learning
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