Recent researches on emotion recognition suggests that domain adaptation, a form of
transfer learning, has the capability to solve the cross-subject problem in Affective brain …

Precise and timely disease diagnosis is essential for making effective treatment decisions
and halting disease progression. Biomedical signals offer the potential for non-invasive …

In the application of brain-computer interfaces (BCIs), electroencephalogram (EEG) signals
are difficult to collect in large quantities due to the non-stationary nature and long calibration …

Electroencephalogram (EEG) data are produced in quantity for measuring brain activity in
response to external stimuli. With the rapid development of brain-inspired intelligence …

Cross-domain speech emotion recognition (SER) is an effective strategy to improve the
generalization ability of emotion classification models, which is an important research …

The statistical variability of electroencephalographic (EEG) signals across individuals poses
a common challenge for brain–computer interfaces (BCI). Specifically, the reuse of pre …

Individual distribution discrepancy poses significant challenges to cross-subject
electroencephalography (EEG) signal decoding. Although transfer learning has emerged as …

Feature adaptation plays crucial roles in the calibration process of motor imagery brain
computer interfaces (MI-BCIs). Due to the temporal varying and spatial coupling …

In the medical field, datasets are mostly integrated across sites due to difficult data
acquisition and insufficient data at a single site. The domain shift problem caused by the …

Motor imagery (MI) brain-computer interface (BCI) paradigms have been extensively used in
neurological rehabilitation. However, due to the required long calibration time and non …