Adaptive transfer learning for EEG motor imagery classification with deep convolutional neural network
In recent years, deep learning has emerged as a powerful tool for developing Brain–
Computer Interface (BCI) systems. However, for deep learning models trained entirely on the …
Computer Interface (BCI) systems. However, for deep learning models trained entirely on the …
Tsception: Capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition
The high temporal resolution and the asymmetric spatial activations are essential attributes
of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the …
of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the …
[HTML][HTML] An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification
Abstract Electroencephalogram (EEG)-based Brain–Computer Interfaces (BCIs) build a
communication path between human brain and external devices. Among EEG-based BCI …
communication path between human brain and external devices. Among EEG-based BCI …
Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users
N Tibrewal, N Leeuwis, M Alimardani - Plos one, 2022 - journals.plos.org
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain
activity patterns associated with mental imagination of movement and convert them into …
activity patterns associated with mental imagination of movement and convert them into …
Tsception: a deep learning framework for emotion detection using EEG
In this paper, we propose a deep learning framework, TSception, for emotion detection from
electroencephalogram (EEG). TSception consists of temporal and spatial convolutional …
electroencephalogram (EEG). TSception consists of temporal and spatial convolutional …
LGGNet: Learning from local-global-graph representations for brain–computer interface
Neuropsychological studies suggest that co-operative activities among different brain
functional areas drive high-level cognitive processes. To learn the brain activities within and …
functional areas drive high-level cognitive processes. To learn the brain activities within and …
A multi-view CNN with novel variance layer for motor imagery brain computer interface
Accurate and robust classification of Motor Imagery (MI) from Electroencephalography (EEG)
signals is among the most challenging tasks in Brain-Computer Interface (BCI) field. To …
signals is among the most challenging tasks in Brain-Computer Interface (BCI) field. To …
FBMSNet: A filter-bank multi-scale convolutional neural network for EEG-based motor imagery decoding
K Liu, M Yang, Z Yu, G Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Object: Motor imagery (MI) is a mental process widely utilized as the experimental paradigm
for brain-computer interfaces (BCIs) across a broad range of basic science and clinical …
for brain-computer interfaces (BCIs) across a broad range of basic science and clinical …
A transfer learning framework based on motor imagery rehabilitation for stroke
F Xu, Y Miao, Y Sun, D Guo, J Xu, Y Wang, J Li, H Li… - Scientific Reports, 2021 - nature.com
Deep learning networks have been successfully applied to transfer functions so that the
models can be adapted from the source domain to different target domains. This study uses …
models can be adapted from the source domain to different target domains. This study uses …
An end-to-end 3D convolutional neural network for decoding attentive mental state
The detection of attentive mental state plays an essential role in the neurofeedback process
and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the …
and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the …