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 …

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 …

Background Since the replication crisis, standardization has become even more important in
psychological science and neuroscience. As a result, many methods are being …

Electroencephalography (EEG) signals can be analyzed in the temporal, spatial, or
frequency domains. Noise and artifacts during the data acquisition phase contaminate these …

A Brain Computer-Interfaces (BCI) is a communication system that enables human brain to
interact with machines or devices without involving physical contact by using EEG signals …

An accruing body of research has shown that interoception (the sensing of signals from the
body's internal milieu) relies on both a direct route (afforded by the vagus nerve) and a …

Recently, many muscle synergy-based human motion prediction models and algorithms
have been proposed. In this study, the muscle synergies extracted from electromyography …

In our life, emotions often have a profound impact on human behavior, especially for drivers,
as negative emotions can increase the risk of traffic accidents. As such, it is imperative to …

Background Contamination of scalp measurement by tonic muscle artefacts, even in resting
positions, is an unavoidable issue in EEG recording. These artefacts add significant energy …