[HTML][HTML] A survey on deep learning-based short/zero-calibration approaches for EEG-based brain–computer interfaces
Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging
technology that enables a communication pathway between a user and an external system …
technology that enables a communication pathway between a user and an external system …
GANSER: A self-supervised data augmentation framework for EEG-based emotion recognition
Electroencephalography (EEG)-based affective computing has a scarcity problem. As a
result, it is difficult to build effective, highly accurate and stable models using machine …
result, it is difficult to build effective, highly accurate and stable models using machine …
Deep convolution generative adversarial network-based electroencephalogram data augmentation for post-stroke rehabilitation with motor imagery
The motor imagery brain–computer interface (MI-BCI) system is currently one of the most
advanced rehabilitation technologies, and it can be used to restore the motor function of …
advanced rehabilitation technologies, and it can be used to restore the motor function of …
[HTML][HTML] Data augmentation strategies for EEG-based motor imagery decoding
The wide use of motor imagery as a paradigm for brain-computer interfacing (BCI) points to
its characteristic ability to generate discriminatory signals for communication and control. In …
its characteristic ability to generate discriminatory signals for communication and control. In …
Multichannel synthetic preictal EEG signals to enhance the prediction of epileptic seizures
Epilepsy is a chronic neurological disorder affecting 1% of people worldwide, deep learning
(DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for …
(DL) algorithms-based electroencephalograph (EEG) analysis provides the possibility for …
[HTML][HTML] Subject-independent eeg classification based on a hybrid neural network
H Zhang, H Ji, J Yu, J Li, L Jin, L Liu, Z Bai… - Frontiers in …, 2023 - frontiersin.org
A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a
novel technology that provides a direct pathway between human brain and outside world …
novel technology that provides a direct pathway between human brain and outside world …
A conditional input-based GAN for generating spatio-temporal motor imagery electroencephalograph data
I Raoof, MK Gupta - Neural Computing and Applications, 2023 - Springer
Abstract Brain Computer Interface is an emerging technology for assisting patients having
long term disability. Electroencephalography is the best technique for recording neural …
long term disability. Electroencephalography is the best technique for recording neural …
Common spatial generative adversarial networks based EEG data augmentation for cross-subject brain-computer interface
Y Song, L Yang, X Jia, L Xie - arXiv preprint arXiv:2102.04456, 2021 - arxiv.org
The cross-subject application of EEG-based brain-computer interface (BCI) has always been
limited by large individual difference and complex characteristics that are difficult to …
limited by large individual difference and complex characteristics that are difficult to …
Motor Imagery Signal Classification using Adversarial Learning: A systematic literature review
S Mishra, O Mahmudi, A Jalali - IEEE Access, 2024 - ieeexplore.ieee.org
This paper presents a comprehensive Systematic Literature Review (SLR) on the utilization
of adversarial learning techniques in Motor Imagery (MI) signal classification, a key …
of adversarial learning techniques in Motor Imagery (MI) signal classification, a key …
[HTML][HTML] Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain–computer interface
Convolutional neural networks (CNNs), which can recognize structural/configuration
patterns in data with different architectures, have been studied for feature extraction …
patterns in data with different architectures, have been studied for feature extraction …