The rapid development of biosensing technologies together with the advent of deep learning
has marked an era in healthcare and biomedical research where widespread devices like …

Identifying the cognitive workload of operators is crucial in complex human-automation
collaboration systems. An excessive workload can lead to fatigue or accidents, while an …

Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals.
Integrating supervised deep learning techniques with EEG signals has recently facilitated …

Self-supervised approaches for electroencephalography (EEG) representation learning face
three specific challenges inherent to EEG data:(1) The low signal-to-noise ratio which …

Cognitive load, the amount of mental effort required for task completion, plays an important
role in performance and decision-making outcomes, making its classification and analysis …

Leveraging multimodal information from biosignals is vital for building a comprehensive
representation of people's physical and mental states. However, multimodal biosignals often …

Electroencephalography (EEG) is a non-invasive technique to measure and record brain
electrical activity, widely used in various BCI and healthcare applications. Early EEG …

Due to the abundant neurophysiological information in the electroencephalogram (EEG)
signal, EEG signals integrated with deep learning methods have gained substantial traction …

Sleep staging has a very important role in diagnosing patients with sleep disorders. In
general, this task is very time-consuming for physicians to perform. Deep learning shows …

In recent years, the field of electroencephalography (EEG) analysis has witnessed
remarkable advancements, driven by the integration of machine learning and artificial …