Feature extraction and classification is a vital part in motor imagery-based brain-computer
interface (BCI) system. Traditional deep learning (DL) methods usually perform better with …

Electroencephalography (EEG) signal classification is a crucial part in motor imagery brain–
computer interface (BCI) system. Traditional supervised learning methods have performed …

Semi-supervised learning (SSL) based on manifold regularization in many fields has
attracted widespread attention and research. However, SSL still has two main challenges …

In machine learning field, Graph-based Semi-Supervised Learning (GSSL) has recently
attracted much attention and many researchers have proposed a number of different …

In order to gain a competitive advantage in the international market, the automatic and
accurate grading of fruit and vegetables, which is grouping fruit by either size, color or other …

Abstract Machine learning algorithms play a critical role in electroencephalograpy (EEG)-
based brain-computer interface (BCI) systems. However, collecting labeled samples for …

Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-
based brain-computer interface (BCI). However, labeled EEG samples are generally scarce …

In many real-world applications, labeled instances are generally limited and expensively
collected, while the most instances are unlabeled and the amount is often sufficient …

Semi-supervised learning (SSL) based on manifold regularization (MR) is an excellent
learning framework. However, the performance of SSL heavily depends on the construction …

Generative adversarial networks are currently used to solve various problems and are one
of the most popular models. Generator and discriminator are characteristics of continuous …