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 …

Reaching out the function of the brain in perceiving input data from the outside world is one
of the great targets of neuroscience. Neural decoding helps us to model the connection …

Interpretation of neural activity in response to stimulations received from the surrounding
environment is necessary to realize automatic brain decoding. Analyzing the brain …

Several imaging modalities and many signal recording techniques have been used to study
the brain activities. Significant advancements in medical device technologies like …

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …

Background and objective: The development of deep learning has led to significant
improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification …

This work presents a novel cross-modality method for acquiring human visual perception on
stereoscopic image quality ranking (SIQR), aiming to learn latent biological representations …

Brain-Computer Interfaces connect the brain to external control devices, necessitating the
accurate translation of brain signals such as from electroencephalography (EEG) into …

Annotated electroencephalogram (EEG) data is the prerequisite for artificial intelligence-
driven EEG autoanalysis. However, the scarcity of annotated data due to its high-cost and …

This work addresses the challenge of classifying multiclass visual EEG signals into 40
classes for brain–computer interface applications using deep learning architectures. The …