A brain computer interface (BCI) framework uses computer algorithms to detect mental
activity patterns and manipulate external devices. Because of its simplicity and non …

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain
based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

EEG signals have been reported to be informative and reliable for emotion recognition in
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …

Brain computer interfaces (BCI) provide a direct communication link between the brain and a
computer or other external devices. They offer an extended degree of freedom either by …

Objective. Previous studies on emotion recognition from electroencephalography (EEG)
mainly rely on single-channel-based feature extraction methods, which ignore the functional …

Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …

Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with
millisecond resolution, rendering it a viable method in neuroscience and healthcare …

In recent years, deep-learning models gained attention for electroencephalography (EEG)
classification tasks due to their excellent performance and ability to extract complex features …

It is vital to develop general models that can be shared across subjects and sessions in the
real-world deployment of electroencephalogram (EEG) emotion recognition systems. Many …