Introduction Brain-computer interfaces (BCIs) have the potential in providing neurofeedback
for stroke patients to improve motor rehabilitation. However, current BCIs often only detect …

The main purpose of this paper is to provide information on how to create a convolutional
neural network (CNN) for extracting features from EEG signals. Our task was to understand …

The accurate evaluation of mental workload of operators in human machine systems is of
great significance in ensuring the safety of operators and the correct execution of tasks …

In this paper, a gait classification method based on the deep belief network (DBN) optimized
by the sparrow search algorithm (SSA) is proposed. The multiple features obtained based …

The utilization of motor imagery-based brain-computer interfaces (MI-BCI) has been shown
to assist stroke patients activate motor regions in the brain. In particular, the brain regions …

Dynamic brain network has the advantage over static brain network in characterizing the
variation pattern of functional brain connectivity, and it has attracted increasing attention in …

We introduce a novel approach to training data augmentation in brain–computer interfaces
(BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs …

Background Unilateral upper limb multitasking brings essential improvements to stroke
rehabilitation and prosthetic control. However, the influence and recognition of multiple tasks …

The paper presents a novel recognition framework for incomplete noninvasive
Electroencephalography (EEG) signals relying on the recent advances in tensor algebra …

Brain–computer interface (BCI) for motor imagery is an advanced technology used in the
field of medical rehabilitation. However, due to the poor accuracy of electroencephalogram …