Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
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 …
made it possible to endow machines/computers with the ability of emotion understanding …
A multi-column CNN model for emotion recognition from EEG signals
H Yang, J Han, K Min - Sensors, 2019 - mdpi.com
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 …
Recently, a deep neural network is widely employed for extracting features and recognizing …
Emotion in a century: A review of emotion recognition
T Thanapattheerakul, K Mao, J Amoranto… - proceedings of the 10th …, 2018 - dl.acm.org
Emotion plays an important role in our daily lives. Ever since the 19th century, experimental
psychologists have attempted to understand and explain human emotion. Despite an …
psychologists have attempted to understand and explain human emotion. Despite an …
Channel selection method for EEG emotion recognition using normalized mutual information
ZM Wang, SY Hu, H Song - IEEE access, 2019 - ieeexplore.ieee.org
Electroencephalography (EEG) signals can reflect activities of the human brain and
represent different emotional states. However, recognizing emotions based on full-channel …
represent different emotional states. However, recognizing emotions based on full-channel …
Respiration-based emotion recognition with deep learning
Different physiological signals are of different origins and may describe different functions of
the human body. This paper studied respiration (RSP) signals alone to figure out its ability in …
the human body. This paper studied respiration (RSP) signals alone to figure out its ability in …
Convolutional neural network approach for EEG-based emotion recognition using brain connectivity and its spatial information
Emotion recognition based on electroencephalography (EEG) has received attention as a
way to implement human-centric services. However, there is still much room for …
way to implement human-centric services. However, there is still much room for …
Fast emotion recognition based on single pulse PPG signal with convolutional neural network
Physiological signals contain considerable information regarding emotions. This paper
investigated the ability of photoplethysmogram (PPG) signals to recognize emotion …
investigated the ability of photoplethysmogram (PPG) signals to recognize emotion …
Emotion recognition based on EEG using generative adversarial nets and convolutional neural network
B Pan, W Zheng - computational and Mathematical Methods in …, 2021 - Wiley Online Library
Emotion recognition plays an important role in the field of human‐computer interaction
(HCI). Automatic emotion recognition based on EEG is an important topic in brain‐computer …
(HCI). Automatic emotion recognition based on EEG is an important topic in brain‐computer …
Emotion recognition using convolutional neural network with selected statistical photoplethysmogram features
Emotion recognition research has been conducted using various physiological signals. In
this paper, we propose an efficient photoplethysmogram-based method that fuses the deep …
this paper, we propose an efficient photoplethysmogram-based method that fuses the deep …
The design of CNN architectures for optimal six basic emotion classification using multiple physiological signals
SJ Oh, JY Lee, DK Kim - Sensors, 2020 - mdpi.com
This study aimed to design an optimal emotion recognition method using multiple
physiological signal parameters acquired by bio-signal sensors for improving the accuracy …
physiological signal parameters acquired by bio-signal sensors for improving the accuracy …