Emotion recognition using EEG has been widely studied to address the challenges
associated with affective computing. Using manual feature extraction methods on EEG …

To achieve a highly efficient brain-computer interface (BCI) system regarding emotion
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature …

Feature extraction of electroencephalography (EEG) signals plays a significant role in the
wearable computing field. Due to the practical applications of EEG emotion calculation …

Rapid advancements in the medical field have drawn much attention to automatic emotion
classification from EEG data. People's emotional states are crucial factors in how they …

Of late, emotion detection from brain signal has become a topic of research. Various
machine learning algorithms have been applied to classify the emotion as a psychological …

Nowadays, many deep models have been presented to recognize emotions using
electroencephalogram (EEG) signals. These deep models are computationally intensive, it …

EEG signal has been widely used in emotion recognition. However, too many channels and
extracted features are used in the current EEG-based emotion recognition methods, which …

Recent advancements in human-computer interaction research have led to the possibility of
emotional communication via brain-computer interface systems for patients with …

Emotion recognition is a challenging problem in Brain‐Computer Interaction (BCI).
Electroencephalogram (EEG) gives unique information about brain activities that are created …

Emotional awareness perception is a largely growing field that allows for more natural
interactions between people and machines. Electroencephalography (EEG) has emerged …