Recognition of human emotions using EEG signals: A review
Assessment of the cognitive functions and state of clinical subjects is an important aspect of
e-health care delivery, and in the development of novel human-machine interfaces. A …
e-health care delivery, and in the development of novel human-machine interfaces. A …
[HTML][HTML] EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques
Emotion recognition based on electroencephalography (EEG) signal features is now one of
the booming big data research areas. As the number of commercial EEG devices in the …
the booming big data research areas. As the number of commercial EEG devices in the …
Emotions recognition using EEG signals: A survey
SM Alarcao, MJ Fonseca - IEEE transactions on affective …, 2017 - ieeexplore.ieee.org
Emotions have an important role in daily life, not only in human interaction, but also in
decision-making processes, and in the perception of the world around us. Due to the recent …
decision-making processes, and in the perception of the world around us. Due to the recent …
Emotion recognition from EEG signal focusing on deep learning and shallow learning techniques
Recently, electroencephalogram-based emotion recognition has become crucial in enabling
the Human-Computer Interaction (HCI) system to become more intelligent. Due to the …
the Human-Computer Interaction (HCI) system to become more intelligent. Due to the …
[HTML][HTML] Emotion recognition based on EEG feature maps through deep learning network
A Topic, M Russo - Engineering Science and Technology, an International …, 2021 - Elsevier
Emotion recognition using electroencephalogram (EEG) signals is getting more and more
attention in recent years. Since the EEG signals are noisy, non-linear and have non …
attention in recent years. Since the EEG signals are noisy, non-linear and have non …
Feature extraction and selection for emotion recognition from EEG
Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a
user, which is considered an important factor in human-machine-interaction. Many methods …
user, which is considered an important factor in human-machine-interaction. Many methods …
Emotion recognition from EEG signals using multidimensional information in EMD domain
N Zhuang, Y Zeng, L Tong, C Zhang… - BioMed research …, 2017 - Wiley Online Library
This paper introduces a method for feature extraction and emotion recognition based on
empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into …
empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into …
Differences first in asymmetric brain: A bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition
Neuroscience research studies have shown that the left and right hemispheres of the human
brain response differently to the same or different emotions. Exploiting this difference in the …
brain response differently to the same or different emotions. Exploiting this difference in the …
PrimePatNet87: prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition
Nowadays, many deep models have been presented to recognize emotions using
electroencephalogram (EEG) signals. These deep models are computationally intensive, it …
electroencephalogram (EEG) signals. These deep models are computationally intensive, it …
Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors
There is currently no standard or widely accepted subset of features to effectively classify
different emotions based on electroencephalogram (EEG) signals. While combining all …
different emotions based on electroencephalogram (EEG) signals. While combining all …