Subject independent emotion recognition using EEG signals employing attention driven neural networks
AS Rajpoot, MR Panicker - Biomedical Signal Processing and Control, 2022 - Elsevier
Electroencephalogram (EEG) based emotional analysis has been employed in medical
science, security and human–computer interaction with good success. In the recent past …
science, security and human–computer interaction with good success. In the recent past …
Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition
Y Cimtay, E Ekmekcioglu - Sensors, 2020 - mdpi.com
The electroencephalogram (EEG) has great attraction in emotion recognition studies due to
its resistance to deceptive actions of humans. This is one of the most significant advantages …
its resistance to deceptive actions of humans. This is one of the most significant advantages …
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 …
Recognition of Emotions Using Multichannel EEG Data and DBN‐GC‐Based Ensemble Deep Learning Framework
H Chao, H Zhi, L Dong, Y Liu - Computational intelligence and …, 2018 - Wiley Online Library
Fusing multichannel neurophysiological signals to recognize human emotion states
becomes increasingly attractive. The conventional methods ignore the complementarity …
becomes increasingly attractive. The conventional methods ignore the complementarity …
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 …
A comparative study of subject-dependent and subject-independent strategies for EEG-based emotion recognition using LSTM network
This paper addresses the problem of EEG-based emotion recognition and classification and
investigates the performance of classifiers for subject-independent and subject-dependent …
investigates the performance of classifiers for subject-independent and subject-dependent …
A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals
K Kamble, J Sengupta - Multimedia Tools and Applications, 2023 - Springer
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 …
Exploring deep learning features for automatic classification of human emotion using EEG rhythms
Emotion recognition (ER) from Electroencephalogram (EEG) signals is a challenging task
due to the non-linearity and non-stationarity nature of EEG signals. Existing feature …
due to the non-linearity and non-stationarity nature of EEG signals. Existing feature …
Deep learning methods for multi-channel EEG-based emotion recognition
Currently, Fourier-based, wavelet-based, and Hilbert-based time–frequency techniques
have generated considerable interest in classification studies for emotion recognition in …
have generated considerable interest in classification studies for emotion recognition in …
Spatial-temporal feature fusion neural network for EEG-based emotion recognition
The temporal and spatial information of electroencephalogram (EEG) are essential for the
emotion recognition model to learn the discriminative features. Hence, we propose a novel …
emotion recognition model to learn the discriminative features. Hence, we propose a novel …