[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 …
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] An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals
Background: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental
disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and …
disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and …
SPWVD-CNN for automated detection of schizophrenia patients using EEG signals
Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunctions,
hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis …
hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis …
Transformers for EEG-based emotion recognition: A hierarchical spatial information learning model
The spatial information of Electroencephalography (EEG) is essential for emotion
recognition model to learn discriminative feature. The convolutional networks and recurrent …
recognition model to learn discriminative feature. The convolutional networks and recurrent …
A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals
Emotion recognition using electroencephalography (EEG) is becoming an interesting topic
among researchers. It has made a remarkable entry in the domain of biomedical, smart …
among researchers. It has made a remarkable entry in the domain of biomedical, smart …
Ensemble machine learning-based affective computing for emotion recognition using dual-decomposed EEG signals
Machine learning (ML)-based algorithms have shown promising results in
electroencephalogram (EEG)-based emotion recognition. This study compares five …
electroencephalogram (EEG)-based emotion recognition. This study compares five …
[HTML][HTML] A systematic review on automated human emotion recognition using electroencephalogram signals and artificial intelligence
Abstract Brain-Computer Interaction (BCI) system intelligence has become more dependent
on electroencephalogram (EEG)-based emotion recognition because of the numerous …
on electroencephalogram (EEG)-based emotion recognition because of the numerous …
A hybrid decision support system for automatic detection of Schizophrenia using EEG signals
Background Schizophrenia (SCZ) is a serious neurological condition in which people suffer
with distorted perception of reality. SCZ may result in a combination of delusions …
with distorted perception of reality. SCZ may result in a combination of delusions …
A dataset for emotion recognition using virtual reality and EEG (DER-VREEG): Emotional state classification using low-cost wearable VR-EEG headsets
Emotions are viewed as an important aspect of human interactions and conversations, and
allow effective and logical decision making. Emotion recognition uses low-cost wearable …
allow effective and logical decision making. Emotion recognition uses low-cost wearable …