AIMC Topic: Brain-Computer Interfaces
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Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.
Sensors (Basel, Switzerland)
Aug 22, 2024
Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imager...
Benchmarking brain-computer interface algorithms: Riemannian approaches vs convolutional neural networks.
Journal of neural engineering
Aug 21, 2024
To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces remains absent from published work. We address this research gap by using MOABB, The Mother Of All BCI ...
User Evaluation of a Shared Robot Control System Combining BCI and Eye Tracking in a Portable Augmented Reality User Interface.
Sensors (Basel, Switzerland)
Aug 14, 2024
This study evaluates an innovative control approach to assistive robotics by integrating brain-computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the aut...
Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification.
Journal of neural engineering
Aug 14, 2024
Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads to overfitting and inadequate generalization capabilities of deep learning-based classification net...
Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.
Neural networks : the official journal of the International Neural Network Society
Aug 8, 2024
Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on a large amount of labeled data to train a satisfactory model for the specific subject, which limits...
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space.
Journal of neural engineering
Aug 8, 2024
This review paper provides an integrated perspective of Explainable Artificial Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use predictive models to interpret brain signals for various high-stake applications. Howev...
Riemannian Locality Preserving Method for Transfer Learning With Applications on Brain-Computer Interface.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Brain-computer interfaces (BCIs) have been widely focused and extensively studied in recent years for their huge prospect of medical rehabilitation and commercial applications. Transfer learning exploits the information in the source domain and appli...
DSFE: Decoding EEG-Based Finger Motor Imagery Using Feature-Dependent Frequency, Feature Fusion and Ensemble Learning.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of ...
Iteratively Calibratable Network for Reliable EEG-Based Robotic Arm Control.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 6, 2024
Robotic arms are increasingly being utilized in shared workspaces, which necessitates the accurate interpretation of human intentions for both efficiency and safety. Electroencephalogram (EEG) signals, commonly employed to measure brain activity, off...
Human-robot interaction in motor imagery: A system based on the STFCN for unilateral upper limb rehabilitation assistance.
Journal of neuroscience methods
Aug 5, 2024
BACKGROUND: Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means th...
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