This paper presents a review of the central theories involved in hybrid models based on
fuzzy systems and artificial neural networks, mainly focused on supervised methods for …

This paper reviews several critical issues facing signal processing for brain–computer
interfaces (BCIs) and suggests several recent approaches that should be further examined …

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network,
the Classification and Regression Tree, and the Random Forest model is proposed, and its …

A brain-computer interface (BCI) for seizure prediction provides a means of controlling
epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected …

This work aims at corroborating the importance and efficacy of mutual learning in motor
imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our …

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy
neural network (RSEFNN) that employs an on-line gradient descent learning rule to address …

Background and objective Multimodal data analysis and large-scale computational
capability is entering medicine in an accelerative fashion and has begun to influence …

The electroencephalogram (EEG) signals tend to have poor time-frequency localization
when analysis techniques involve a fixed set of basis functions such as in short-time Fourier …

One big challenge that hinders the transition of brain-computer interfaces (BCIs) from
laboratory settings to real-life applications is the availability of high-performance and robust …

A common assumption in traditional supervised learning is the similar probability distribution
of data between the training phase and the testing/operating phase. When transitioning from …