[HTML][HTML] Emotion recognition and artificial intelligence: A systematic review (2014–2023) and research recommendations
Emotion recognition is the ability to precisely infer human emotions from numerous sources
and modalities using questionnaires, physical signals, and physiological signals. Recently …
and modalities using questionnaires, physical signals, and physiological signals. Recently …
Emotion recognition in EEG signals using deep learning methods: A review
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 …
planning, reasoning, and other mental states. As a result, they are considered a significant …
EEG emotion recognition based on the attention mechanism and pre-trained convolution capsule network
S Liu, Z Wang, Y An, J Zhao, Y Zhao… - Knowledge-Based Systems, 2023 - Elsevier
Given the rapid development of brain–computer interfaces, emotion identification based on
EEG signals has emerged as a new study area with tremendous importance in recent years …
EEG signals has emerged as a new study area with tremendous importance in recent years …
Deep imbalanced learning for multimodal emotion recognition in conversations
The main task of multimodal emotion recognition in conversations (MERC) is to identify the
emotions in modalities, eg, text, audio, image, and video, which is a significant development …
emotions in modalities, eg, text, audio, image, and video, which is a significant development …
Deep learning-based approach for emotion recognition using electroencephalography (EEG) signals using bi-directional long short-term memory (Bi-LSTM)
Emotions are an essential part of daily human communication. The emotional states and
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …
EEG-based emotion recognition using spatial-temporal graph convolutional LSTM with attention mechanism
L Feng, C Cheng, M Zhao, H Deng… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
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 …
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …
Automatic eyeblink and muscular artifact detection and removal from EEG signals using k-nearest neighbor classifier and long short-term memory networks
Electroencephalogram (EEG) is often corrupted with artifacts originating from sources such
as eyes and muscles. Hybrid artifact removal methods often require human intervention for …
as eyes and muscles. Hybrid artifact removal methods often require human intervention for …
DeepThink IoT: the strength of deep learning in internet of things
Abstract The integration of Deep Learning (DL) and the Internet of Things (IoT) has
revolutionized technology in the twenty-first century, enabling humans and machines to …
revolutionized technology in the twenty-first century, enabling humans and machines to …
A transformer-based deep neural network model for SSVEP classification
Steady-state visual evoked potential (SSVEP) is one of the most commonly used control
signals in the brain–computer interface (BCI) systems. However, the conventional spatial …
signals in the brain–computer interface (BCI) systems. However, the conventional spatial …
EEG based emotion detection using fourth order spectral moment and deep learning
This paper proposes emotion detection using Electroencephalography (EEG) signal based
on Linear Formulation of Differential Entropy (LF-D f E) feature extractor and BiLSTM …
on Linear Formulation of Differential Entropy (LF-D f E) feature extractor and BiLSTM …