Deep learning for sensor-based human activity recognition: Overview, challenges, and opportunities
The vast proliferation of sensor devices and Internet of Things enables the applications of
sensor-based activity recognition. However, there exist substantial challenges that could …
sensor-based activity recognition. However, there exist substantial challenges that could …
Data augmentation for deep neural networks model in EEG classification task: a review
Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic
oscillations of neural activity, which is one of the core technologies of brain-computer …
oscillations of neural activity, which is one of the core technologies of brain-computer …
EEG-based emotion recognition via channel-wise attention and self attention
Emotion recognition based on electroencephalography (EEG) is a significant task in the
brain-computer interface field. Recently, many deep learning-based emotion recognition …
brain-computer interface field. Recently, many deep learning-based emotion recognition …
Emotion recognition from multi-channel EEG via deep forest
Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks
based on electroencephalography (EEG), and have achieved better performance than …
based on electroencephalography (EEG), and have achieved better performance than …
Self‐training maximum classifier discrepancy for EEG emotion recognition
X Zhang, D Huang, H Li, Y Zhang… - CAAI Transactions on …, 2023 - Wiley Online Library
Even with an unprecedented breakthrough of deep learning in electroencephalography
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …
(EEG), collecting adequate labelled samples is a critical problem due to laborious and time …
Transformer-based spatial-temporal feature learning for EEG decoding
Y Song, X Jia, L Yang, L Xie - arXiv preprint arXiv:2106.11170, 2021 - arxiv.org
At present, people usually use some methods based on convolutional neural networks
(CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in …
(CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in …
Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals
Emotion is interpreted as a psycho-physiological process, and it is associated with
personality, behavior, motivation, and character of a person. The objective of affective …
personality, behavior, motivation, and character of a person. The objective of affective …
EEG-based emotion recognition via transformer neural architecture search
C Li, Z Zhang, X Zhang, G Huang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Emotion recognition based on electroencephalogram (EEG) plays an increasingly important
role in the field of brain–computer interfaces. Recently, deep learning has been widely …
role in the field of brain–computer interfaces. Recently, deep learning has been widely …
Motor imagery classification via temporal attention cues of graph embedded EEG signals
Motor imagery classification from EEG signals is essential for motor rehabilitation with a
Brain-Computer Interface (BCI). Most current works on this issue require a subject-specific …
Brain-Computer Interface (BCI). Most current works on this issue require a subject-specific …
Spatial-frequency convolutional self-attention network for EEG emotion recognition
Recently, the combination of neural network and attention mechanism is widely employed
for electroencephalogram (EEG) emotion recognition (EER) and has achieved remarkable …
for electroencephalogram (EEG) emotion recognition (EER) and has achieved remarkable …