Object: Transformer-based neural networks have been applied to the
electroencephalography (EEG) decoding for motor imagery (MI). However, most networks …

Background Emerging brain-computer interface (BCI) technology holds promising potential
to enhance the quality of life for individuals with disabilities. Nevertheless, the constrained …

Recent studies on brain–computer interfaces (BCIs) implemented in robotic systems have
shown that the system's effectiveness in assisting individuals with movement disorders to …

Research in brain-computer interface (BCI), particularly in brain intention decoding, has
made significant strides relying on the remarkable capabilities of deep learning (DL) …

Multi-view learning is a rapidly evolving research area focused on develo** diverse
learning representations. In neural data analysis, this approach holds immense potential by …

Robust decoding performance is essential for the practical deployment of brain-computer
interface (BCI) systems. Existing EEG decoding models often rely on large amounts of …

Situation awareness (SA) is directly related to the operating level of dynamic system
operators, and electroencephalography (EEG) is frequently employed as the gold standard …

In the field of motor imagery (MI) recognition based on electroencephalogram (EEG),
complex network-based analysis of brain connectivity has gained significant attention …

This study examines the feasibility of using event-related potentials (ERPs) obtained from
electroencephalographic (EEG) recordings as biomarkers for long-term memory item …

Cognitive Language Processing (CLP), situated at the intersection of Natural Language
Processing (NLP) and cognitive science, plays a progressively pivotal role in the domains of …