AIMC Topic: Electroencephalography
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Artificial neural network for evaluating sleep spindles and slow waves after transcranial magnetic stimulation in a child with autism.
Neurocase
Dec 4, 2024
Sleep spindles (SS) and slow waves (SW) serve as indicators of the integrity of thalamocortical connections, which are often compromised in individuals with autism spectrum disorder (ASD). Transcranial magnetic stimulation (TMS) can modulate brain ac...
Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain.
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2024
In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, ti...
On the role of visual feedback and physiotherapist-patient interaction in robot-assisted gait training: an eye-tracking and HD-EEG study.
Journal of neuroengineering and rehabilitation
Dec 3, 2024
BACKGROUND: Treadmill based Robotic-Assisted Gait Training (t-RAGT) provides for automated locomotor training to help the patient achieve a physiological gait pattern, reducing the physical effort required by therapist. By introducing the robot as a ...
Intra- and inter-channel deep convolutional neural network with dynamic label smoothing for multichannel biosignal analysis.
Neural networks : the official journal of the International Neural Network Society
Dec 2, 2024
Efficient processing of multichannel biosignals has significant application values in the fields of healthcare and human-machine interaction. Although previous research has achieved high recognition performance with deep convolutional neural networks...
Hybrid Network Using Dynamic Graph Convolution and Temporal Self-Attention for EEG-Based Emotion Recognition.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
The electroencephalogram (EEG) signal has become a highly effective decoding target for emotion recognition and has garnered significant attention from researchers. Its spatial topological and time-dependent characteristics make it crucial to explore...
Attention-Based Multimodal tCNN for Classification of Steady-State Visual Evoked Potentials and Its Application to Gripper Control.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
The classification problem for short time-window steady-state visual evoked potentials (SSVEPs) is important in practical applications because shorter time-window often means faster response speed. By combining the advantages of the local feature lea...
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...
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...
A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain.
IEEE transactions on neural networks and learning systems
Dec 2, 2024
Humans show a remarkable ability in solving the cocktail party problem. Decoding auditory attention from the brain signals is a major step toward the development of bionic ears emulating human capabilities. Electroencephalography (EEG)-based auditory...
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...
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