Review of EEG-based biometrics in 5G-IoT: Current trends and future prospects
The increasing integration of the Internet of Things (IoT) into daily life has led to significant
changes in our social interactions. The advent of innovative IoT solutions, combined with the …
changes in our social interactions. The advent of innovative IoT solutions, combined with the …
EEG-GCN: spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition
Y Gao, X Fu, T Ouyang, Y Wang - IEEE Signal Processing …, 2022 - ieeexplore.ieee.org
Graph networks are naturally suitable for modeling multi-channel features of EEG signals.
However, the existing study that attempts to utilize graph-based neural networks for EEG …
However, the existing study that attempts to utilize graph-based neural networks for EEG …
Cardiac artifact noise removal from sleep EEG signals using hybrid denoising model
Sleep is one of the prime natural activities for human well-being in physical, emotional, and
mental aspects. The assessment of sleep electroencephalography (EEG) signals is required …
mental aspects. The assessment of sleep electroencephalography (EEG) signals is required …
A GAN guided parallel CNN and transformer network for EEG denoising
Electroencephalography (EEG) signals are often contaminated with various physiological
artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts …
artifacts, seriously affecting the quality of subsequent analysis. Therefore, removing artifacts …
Safe-level SMOTE method for handling the class imbalanced problem in electroencephalography dataset of adult anxious state
SNSS Daud, R Sudirman, TW Shing - Biomedical Signal Processing and …, 2023 - Elsevier
Anxiety disorder is a mental state in which a person experiences excessive worry, fear,
nervousness, and apprehension. Measuring brain signals using the …
nervousness, and apprehension. Measuring brain signals using the …
A method for optimizing the artifact subspace reconstruction performance in low-density EEG
A Cataldo, S Criscuolo, E De Benedetto… - IEEE Sensors …, 2022 - ieeexplore.ieee.org
Electroencephalogram (EEG) plays a significant role in the analysis of cerebral activity,
although the recorded electrical brain signals are always contaminated with artifacts. This …
although the recorded electrical brain signals are always contaminated with artifacts. This …
Reliable fault diagnosis using evidential aggregated residual network under varying working conditions and noise interference
Deep neural networks (DNNs) have been widely used for intelligent fault diagnosis under
the implicit assumption that the testing data draws from the same distribution of the training …
the implicit assumption that the testing data draws from the same distribution of the training …
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 electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer
X Wang, X Dai, Y Liu, X Chen, Q Hu, R Hu… - Frontiers in Human …, 2023 - frontiersin.org
Introduction Motor imagery electroencephalography (MI-EEG) has significant application
value in the field of rehabilitation, and is a research hotspot in the brain-computer interface …
value in the field of rehabilitation, and is a research hotspot in the brain-computer interface …
Automated ocular artifacts removal framework based on adaptive chirp mode decomposition
The removal of ocular artifacts (OAs) from electroencephalogram (EEG) signals is crucial for
effective and accurate analysis of the signals in different neurological and brain computer …
effective and accurate analysis of the signals in different neurological and brain computer …