In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …

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

The spatial information of Electroencephalography (EEG) is essential for emotion
recognition model to learn discriminative feature. The convolutional networks and recurrent …

Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks
based on electroencephalography (EEG), and have achieved better performance than …

EEG-based automatic emotion recognition can help brain-inspired robots in improving their
interactions with humans. This paper presents a novel framework for emotion recognition …

The temporal and spatial information of electroencephalogram (EEG) are essential for the
emotion recognition model to learn the discriminative features. Hence, we propose a novel …

Neural network (NN) finds role in variety of applications due to combined effect of feature
extraction and classification availability in deep learning algorithms. In this paper, we have …

We present a multi-column CNN-based model for emotion recognition from EEG signals.
Recently, a deep neural network is widely employed for extracting features and recognizing …

Emotion recognition is a crucial application in human–computer interaction. It is usually
conducted using facial expressions as the main modality, which might not be reliable. In this …

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural
networks and can be applied to characterize information propagation patterns for different …