An EEG database and its initial benchmark emotion classification performance
A Seal, PPN Reddy, P Chaithanya… - … methods in medicine, 2020 - Wiley Online Library
Human emotion recognition has been a major field of research in the last decades owing to
its noteworthy academic and industrial applications. However, most of the state‐of‐the‐art …
its noteworthy academic and industrial applications. However, most of the state‐of‐the‐art …
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 recognition based on higher order statistics and deep learning algorithm
The objective of this paper is online recognition of human emotions based on
electroencephalogram (EEG) signals. The emotions are originated from the central and …
electroencephalogram (EEG) signals. The emotions are originated from the central and …
Feature extraction and selection for emotion recognition from EEG
Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a
user, which is considered an important factor in human-machine-interaction. Many methods …
user, which is considered an important factor in human-machine-interaction. Many methods …
EEG-based automatic emotion recognition: Feature extraction, selection and classification methods
P Ackermann, C Kohlschein, JA Bitsch… - 2016 IEEE 18th …, 2016 - ieeexplore.ieee.org
Automatic emotion recognition is an interdisciplinary research field which deals with the
algorithmic detection of human affect, eg anger or sadness, from a variety of sources, such …
algorithmic detection of human affect, eg anger or sadness, from a variety of sources, such …
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 …
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 …
EEG-based emotion analysis using non-linear features and ensemble learning approaches
Recognition of emotions based on electroencephalography (EEG) has become one of the
most emerging topics for healthcare, education system, knowledge sharing, gaming, and …
most emerging topics for healthcare, education system, knowledge sharing, gaming, and …
EEG-based emotion classification using a deep neural network and sparse autoencoder
Emotion classification based on brain–computer interface (BCI) systems is an appealing
research topic. Recently, deep learning has been employed for the emotion classifications of …
research topic. Recently, deep learning has been employed for the emotion classifications of …
EEG-based emotion recognition using logistic regression with Gaussian kernel and Laplacian prior and investigation of critical frequency bands
Emotion plays a nuclear part in human attention, decision-making, and communication.
Electroencephalogram (EEG)-based emotion recognition has developed a lot due to the …
Electroencephalogram (EEG)-based emotion recognition has developed a lot due to the …