A review on transfer learning in EEG signal analysis
Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
EEG‐based emotion recognition: a state‐of‐the‐art review of current trends and opportunities
NS Suhaimi, J Mountstephens… - Computational …, 2020 - Wiley Online Library
Emotions are fundamental for human beings and play an important role in human cognition.
Emotion is commonly associated with logical decision making, perception, human …
Emotion is commonly associated with logical decision making, perception, human …
BENDR: Using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Deep neural networks (DNNs) used for brain–computer interface (BCI) classification are
commonly expected to learn general features when trained across a variety of contexts, such …
commonly expected to learn general features when trained across a variety of contexts, such …
A survey on negative transfer
Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …
facilitate learning in a target domain. It is particularly useful when the target domain has very …
Transfer learning for EEG-based brain–computer interfaces: A review of progress made since 2016
A brain–computer interface (BCI) enables a user to communicate with a computer directly
using brain signals. The most common noninvasive BCI modality, electroencephalogram …
using brain signals. The most common noninvasive BCI modality, electroencephalogram …
Few-shot object detection: A survey
Deep learning approaches have recently raised the bar in many fields, from Natural
Language Processing to Computer Vision, by leveraging large amounts of data. However …
Language Processing to Computer Vision, by leveraging large amounts of data. However …
Alzheimer's disease classification using pre-trained deep networks
JV Shanmugam, B Duraisamy, BC Simon… - … Signal Processing and …, 2022 - Elsevier
Alzheimer disease (AD) is a progressive neurologic disorder that causes the brain to shrink
(atrophy) and brain cells to die. Alzheimer disease is the most common cause of dementia …
(atrophy) and brain cells to die. Alzheimer disease is the most common cause of dementia …
CWT based transfer learning for motor imagery classification for brain computer interfaces
Background The processing of brain signals for Motor imagery (MI) classification to have
better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional …
better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional …
A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition
With the rapid development of artificial intelligence and mobile Internet, the new
requirements for human-computer interaction have been put forward. The personalized …
requirements for human-computer interaction have been put forward. The personalized …
Can emotion be transferred?—A review on transfer learning for EEG-based emotion recognition
W Li, W Huan, B Hou, Y Tian, Z Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The issue of electroencephalogram (EEG)-based emotion recognition has great academic
and practical significance. Currently, there are numerous research trying to address this …
and practical significance. Currently, there are numerous research trying to address this …