Emotion classification via Electroencephalography (EEG) is used to find the relationships
between EEG signals and human emotions. There are many available channels, which …

Emotion recognition from EEG signals is a major field of research in cognitive computing.
The major challenges involved in the task are extracting meaningful features from the …

Emotions were considered an important component. Everyday lives need emotions on a
regular basis. Electroencephalogram (EEG)-based emotion identification was gaining …

Emotions have a vital role in the day-to-day life of human beings, the need and importance
of emotion recognition systems have increased with the role of human computer interface …

In the domain of neuroscience, electroencephalography (EEG) holds a pivotal role in
determining the inner workings of the human brain, offering real-time insights into cognitive …

In this article, we propose a novel feature selection method based on hybrid neural networks
for emotional classification avoiding expensive computation. With the development of neural …

The purpose of this study is to improve human emotional classification accuracy using a
convolution neural networks (CNN) model and to suggest an overall method to classify …

Recently, electroencephalogram-based emotion recognition has become crucial in enabling
the Human-Computer Interaction (HCI) system to become more intelligent. Due to the …

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 …

Recently, the availability of large EEG datasets, advancements in Brain-Computer interface
(BCI) systems and Machine Learning have led to the implementation of deep learning …