Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …

Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

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 …

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact
with the environment. Recent advancements in technology and machine learning algorithms …

In recent years, deep learning (DL) techniques, and in particular convolutional neural
networks (CNNs), have shown great potential in electroencephalograph (EEG)-based …

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

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 …

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

The dynamic uncertain relationship among each brain region is a necessary factor that limits
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …

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 …