For several decades, electroencephalography (EEG) has featured as one of the most
commonly used tools in emotional state recognition via monitoring of distinctive brain …

Giving emotional intelligence to machines can facilitate the early detection and prediction of
mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition …

In this study, we conducted a systematic literature review of 107 primary studies conducted
between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to …

Emotion recognition from electroencephalography (EEG) has been an important research
direction in affective brain–computer interactions (ABCI). An integration of EEG-based …

Recent developments and studies in brain-computer interface (BCI) technologies have
facilitated emotion detection and classification. Many BCI studies have sought to investigate …

Affecting computing is an artificial intelligence area of study that recognizes, interprets,
processes, and simulates human affects. The user's emotional states can be sensed through …

Affective computing, a subcategory of artificial intelligence, detects, processes, interprets,
and mimics human emotions. Thanks to the continued advancement of portable non …

Recognizing cross-subject emotions based on brain imaging data, eg, EEG, has always
been difficult due to the poor generalizability of features across subjects. Thus …

The electroencephalogram (EEG) signal is a medium to realize a brain–computer interface
(BCI) system due to its zero clinical risk and portable acquisition devices. As deep learning …

Due to a large number of potential applications, a good deal of effort has been recently
made toward creating machine learning models that can recognize evoked emotions from …