AIMC Topic: Brain-Computer Interfaces
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Effective Emotion Recognition by Learning Discriminative Graph Topologies in EEG Brain Networks.
IEEE transactions on neural networks and learning systems
Aug 5, 2024
Multichannel electroencephalogram (EEG) is an array signal that represents brain neural networks and can be applied to characterize information propagation patterns for different emotional states. To reveal these inherent spatial graph features and i...
EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification.
Neuroscience
Aug 3, 2024
Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems,...
Toward calibration-free motor imagery brain-computer interfaces: a VGG-based convolutional neural network and WGAN approach.
Journal of neural engineering
Jul 31, 2024
Motor imagery (MI) represents one major paradigm of Brain-computer interfaces (BCIs) in which users rely on their electroencephalogram (EEG) signals to control the movement of objects. However, due to the inter-subject variability, MI BCIs require re...
Decoding lower-limb kinematic parameters during pedaling tasks using deep learning approaches and EEG.
Medical & biological engineering & computing
Jul 19, 2024
Stroke is a neurological condition that usually results in the loss of voluntary control of body movements, making it difficult for individuals to perform activities of daily living (ADLs). Brain-computer interfaces (BCIs) integrated into robotic sys...
The SSHVEP Paradigm-Based Brain Controlled Method for Grasping Robot Using MVMD Combined CNN Model.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 19, 2024
In recent years, the steady-state visual evoked potentials (SSVEP) based brain control method has been employed to help people with disabilities because of its advantages of high information transmission rate and low training time. However, the exist...
A Least-Square Unified Framework for Spatial Filtering in SSVEP-Based BCIs.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jul 11, 2024
The steady-state visual evoked potential (SSVEP) has become one of the most prominent BCI paradigms with high information transfer rate, and has been widely applied in rehabilitation and assistive applications. This paper proposes a least-square (LS)...
LGGNet: Learning From Local-Global-Graph Representations for Brain-Computer Interface.
IEEE transactions on neural networks and learning systems
Jul 8, 2024
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-glob...
Multi-View Multi-Label Fine-Grained Emotion Decoding From Human Brain Activity.
IEEE transactions on neural networks and learning systems
Jul 8, 2024
Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity patte...
An auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials.
Journal of neural engineering
Jul 5, 2024
Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with...
Temporal-spatial cross attention network for recognizing imagined characters.
Scientific reports
Jul 4, 2024
Previous research has primarily employed deep learning models such as Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for decoding imagined character signals. These approaches have treated the temporal and spatial features ...
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