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 …
A fuzzy ensemble-based deep learning model for EEG-based emotion recognition
Emotion recognition from EEG signals is a major field of research in cognitive computing.
The major challenges involved in the task are extracting meaningful features from the …
The major challenges involved in the task are extracting meaningful features from the …
Automated emotion identification using Fourier–Bessel domain-based entropies
Human dependence on computers is increasing day by day; thus, human interaction with
computers must be more dynamic and contextual rather than static or generalized. The …
computers must be more dynamic and contextual rather than static or generalized. The …
Temporal relative transformer encoding cooperating with channel attention for EEG emotion analysis
Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-
computer fusion. EEG signals have inherent temporal and spatial characteristics. However …
computer fusion. EEG signals have inherent temporal and spatial characteristics. However …
[HTML][HTML] Electroencephalography based emotion detection using ensemble classification and asymmetric brain activity
Over the past decade, emotion detection using rhythmic brain activity has become a critical
area of research. The asymmetrical brain activity has garnered the most significant level of …
area of research. The asymmetrical brain activity has garnered the most significant level of …
EEG-based emotion recognition using MobileNet Recurrent Neural Network with time-frequency features
Despite the developments in deep learning, extracting different features from brain signals
remains a crucial challenge in EEG-based emotion recognition. This study introduces a …
remains a crucial challenge in EEG-based emotion recognition. This study introduces a …
A channel selection method to find the role of the amygdala in emotion recognition avoiding conflict learning in EEG signals
Emotion recognition using electroencephalogram signals has been widely studied in the last
decade, achieving artificial intelligence models that accurately classify primitive or primary …
decade, achieving artificial intelligence models that accurately classify primitive or primary …
Simplified 2D CNN architecture with channel selection for emotion recognition using EEG spectrogram
Emotion Recognition through electroencephalography (EEG) is one of the prevailing
emotion recognition techniques achieving higher accuracy rates. Nevertheless, one of the …
emotion recognition techniques achieving higher accuracy rates. Nevertheless, one of the …
Emotion recognition from multichannel EEG signals based on low-rank subspace self-representation features
Y Gao, Y Xue, J Gao - Biomedical Signal Processing and Control, 2025 - Elsevier
In recent years, emotion recognition based on electroencephalogram (EEG) has become the
research focus in human–computer interaction (HCI), but deficiencies in EEG feature …
research focus in human–computer interaction (HCI), but deficiencies in EEG feature …
Simplicial Homology Global Optimization of EEG Signal Extraction for Emotion Recognition
Emotion recognition is a vital part of human functioning. textcolorredIt enables individuals to
respond suitably to environmental events and develop self-awareness. The fast-paced …
respond suitably to environmental events and develop self-awareness. The fast-paced …