Review on emotion recognition based on electroencephalography
H Liu, Y Zhang, Y Li, X Kong - Frontiers in Computational …, 2021 - frontiersin.org
Emotions are closely related to human behavior, family, and society. Changes in emotions
can cause differences in electroencephalography (EEG) signals, which show different …
can cause differences in electroencephalography (EEG) signals, which show different …
Representation learning and pattern recognition in cognitive biometrics: a survey
Cognitive biometrics is an emerging branch of biometric technology. Recent research has
demonstrated great potential for using cognitive biometrics in versatile applications …
demonstrated great potential for using cognitive biometrics in versatile applications …
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 …
Tsception: Capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition
The high temporal resolution and the asymmetric spatial activations are essential attributes
of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the …
of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the …
Locally robust EEG feature selection for individual-independent emotion recognition
Z Yin, L Liu, J Chen, B Zhao, Y Wang - Expert Systems with Applications, 2020 - Elsevier
Brain computer interface (BCI) systems can decode brain affective activities into
interpretable features and facilitate emotional human–computer interaction. However …
interpretable features and facilitate emotional human–computer interaction. However …
MTLFuseNet: a novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning
R Li, C Ren, Y Ge, Q Zhao, Y Yang, Y Shi… - Knowledge-Based …, 2023 - Elsevier
How to extract discriminative latent feature representations from electroencephalography
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …
An EEG data processing approach for emotion recognition
As the most direct way to measure the true emotional states of humans, EEG-based emotion
recognition has been widely used in affective computing applications. In this paper, we aim …
recognition has been widely used in affective computing applications. In this paper, we aim …
Subject independent emotion recognition system for people with facial deformity: an EEG based approach
Emotion recognition from Electroencephalography (EEG) is a better choice for the people
with facial deformity like where facial data is not accurate or not available for example …
with facial deformity like where facial data is not accurate or not available for example …
[HTML][HTML] High-pass filtering artifacts in multivariate classification of neural time series data
Abstract Background Traditionally, EEG/MEG data are high-pass filtered and baseline-
corrected to remove slow drifts. Minor deleterious effects of high-pass filtering in traditional …
corrected to remove slow drifts. Minor deleterious effects of high-pass filtering in traditional …
EEGFuseNet: Hybrid unsupervised deep feature characterization and fusion for high-dimensional EEG with an application to emotion recognition
How to effectively and efficiently extract valid and reliable features from high-dimensional
electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic …
electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic …