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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 …
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 …
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 …
A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic …
communication through the utilization of neural activity generated due to kinesthetic …
Improved domain adaptation network based on Wasserstein distance for motor imagery EEG classification
Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in
brain-computer interface (BCI) technology have facilitated the detection of MI from …
brain-computer interface (BCI) technology have facilitated the detection of MI from …
Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users
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 …
Deep representation-based domain adaptation for nonstationary EEG classification
In the context of motor imagery, electroencephalography (EEG) data vary from subject to
subject such that the performance of a classifier trained on data of multiple subjects from a …
subject such that the performance of a classifier trained on data of multiple subjects from a …
A temporal-spectral-based squeeze-and-excitation feature fusion network for motor imagery EEG decoding
Motor imagery (MI) electroencephalography (EEG) decoding plays an important role in brain-
computer interface (BCI), which enables motor-disabled patients to communicate with the …
computer interface (BCI), which enables motor-disabled patients to communicate with the …
[HTML][HTML] An adversarial discriminative temporal convolutional network for EEG-based cross-domain emotion recognition
Abstract Domain adaptation (DA) tackles the problem where data from the source domain
and target domain have different underlying distributions. In cross-domain (cross-subject or …
and target domain have different underlying distributions. In cross-domain (cross-subject or …
MIN2Net: End-to-end multi-task learning for subject-independent motor imagery EEG classification
Objective: Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow
control of several applications by decoding neurophysiological phenomena, which are …
control of several applications by decoding neurophysiological phenomena, which are …