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 …

Due to the limited perceptual field, convolutional neural networks (CNN) only extract local
temporal features and may fail to capture long-term dependencies for EEG decoding. In this …

This review focuses on electroencephalogram (EEG) acquisition and feedback technology
and its core elements, including the composition and principles of the acquisition devices, a …

In this paper, we propose simultaneous and sequential hybrid brain-computer interfaces
(hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) …

Granger causality-based effective brain connectivity provides a powerful tool to probe the
neural mechanism for information processing and the potential features for brain computer …

With brain-computer interfaces (BCIs), people can send information directly from their brains
to computers. People can use BCIs to send messages or commands without moving. In …

Objective. The electroencephalography (EEG)-based brain–computer interfaces (BCIs) have
been used in the control of robotic arms. The performance of non-invasive BCIs may not be …

In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs),
various spatial filtering methods based on individual calibration data have been proposed to …

Brain–computer interfaces (BCI) are a type of assistive technology that uses the brain
signals of users to establish a communication and control channel between them and an …

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI)
system for home automation control. Over the past decade, BCIs represent a promising …