Cross-subject mental workload recognition using bi-classifier domain adversarial learning.

To deploy Electroencephalogram (EEG) based Mental Workload Recognition (MWR) systems in the real world, it is crucial to develop general models that can be applied across subjects. Previous studies have utilized domain adaptation to mitigate inter-subject discrepancies in EEG data distributions. However, they have focused on reducing global domain discrepancy, while neglecting local workload-categorical domain divergence.

A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users.

MTRT: Motion Trajectory Reconstruction Transformer for EEG-Based BCI Decoding.

Brain computer interface (BCI) is a system that directly uses brain neural activities to communicate with the outside world. Recently, the decoding of the human upper limb based on electroencephalogram (EEG) signals has become an important research branch of BCI. Even though existing research models are capable of decoding upper limb trajectories, the performance needs to be improved to make them more practical for real-world applications.

A Spiking Neural Network with Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces.

Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. However, most existing methods do not effectively mine the specific spatial topology of EEG channels and temporal dependencies of the encoded EEG spikes.

A Temporal-Spectral Fused and Attention-based Deep Model for Automatic Sleep Staging.

Sleep staging is a vital process for evaluating sleep quality and diagnosing sleep-related diseases. Most of the existing automatic sleep staging methods focus on time-domain information and often ignore the transformation relationship between sleep stages. To deal with the above problems, we propose a Temporal-Spectral fused and Attention-based deep neural Network model (TSA-Net) for automatic sleep staging, using a single-channel electroencephalogram (EEG) signal.

Cross-Task Cognitive Workload Recognition Based on EEG and Domain Adaptation.

Cognitive workload recognition is pivotal to maintain the operator's health and prevent accidents in the human-robot interaction condition. So far, the focus of workload research is mostly restricted to a single task, yet cross-task cognitive workload recognition has remained a challenge. Furthermore, when extending to a new workload condition, the discrepancy of electroencephalogram (EEG) signals across various cognitive tasks limits the generalization of the existed model.

Neural Decoding of Chinese Sign Language With Machine Learning for Brain-Computer Interfaces.

Limb motion decoding is an important part of brain-computer interface (BCI) research. Among the limb motion, sign language not only contains rich semantic information and abundant maneuverable actions but also provides different executable commands. However, many researchers focus on decoding the gross motor skills, such as the decoding of ordinary motor imagery or simple upper limb movements.

An Empirical Comparative Study on the Two Methods of Eliciting Singers' Emotions in Singing: Self-Imagination and VR Training.

Emotional singing can affect vocal performance and the audience's engagement. Chinese universities use traditional training techniques for teaching theoretical and applied knowledge. Self-imagination is the predominant training method for emotional singing.