scholarly journals Emotion recognition based on EEG features in movie clips with channel selection

2017 ◽  
Vol 4 (4) ◽  
pp. 241-252 ◽  
Author(s):  
Mehmet Siraç Özerdem ◽  
Hasan Polat
Keyword(s):  
Emotion Recognition ◽  
Channel Selection ◽  
Eeg Features

Emotion recognition based on 3D fuzzy visual and EEG features in movie clips

2014 ◽  
Vol 144 ◽  
pp. 560-568 ◽  
Author(s):  
Giyoung Lee ◽  
Mingu Kwon ◽  
Swathi Kavuri Sri ◽  
Minho Lee
Keyword(s):  
Emotion Recognition ◽  
Eeg Features

scholarly journals Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

2017 ◽  
Vol 21 (6) ◽  
pp. 1003-1013 ◽  
Author(s):  
M. L. R. Menezes ◽  
A. Samara ◽  
L. Galway ◽  
A. Sant’Anna ◽  
A. Verikas ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Virtual Environments ◽  
Benchmark Dataset ◽  
Eeg Features

scholarly journals EEG channel selection strategy for deep learning in emotion recognition

2021 ◽  
Vol 192 ◽  
pp. 2789-2796
Author(s):  
Aleksandra Dura ◽  
Agnieszka Wosiak
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Channel Selection ◽  
Selection Strategy

scholarly journals Cross-Individual Affective Detection Using EEG Signals with Audio-Visual Embedding

2021 ◽  
Author(s):  
Zhen Liang ◽  
Xihao Zhang ◽  
Rushuang Zhou ◽  
Li Zhang ◽  
Linling Li ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Temporal Dynamics ◽  
Recognition Performance ◽  
Critical Issue ◽  
Feature Representation ◽  
Fusion Model ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Brain Data ◽  
Eeg Features

How to effectively and efficiently extract valid and reliable features from high-dimensional electroencephalography (EEG), particularly how to fuse the spatial and temporal dynamic brain information into a better feature representation, is a critical issue in brain data analysis. Most current EEG studies work in a task driven manner and explore the valid EEG features with a supervised model, which would be limited by the given labels to a great extent. In this paper, we propose a practical hybrid unsupervised deep convolutional recurrent generative adversarial network based EEG feature characterization and fusion model, which is termed as EEGFuseNet. EEGFuseNet is trained in an unsupervised manner, and deep EEG features covering both spatial and temporal dynamics are automatically characterized. Comparing to the existing features, the characterized deep EEG features could be considered to be more generic and independent of any specific EEG task. The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases. The results demonstrate the proposed EEGFuseNet is a robust and reliable model, which is easy to train and performs efficiently in the representation and fusion of dynamic EEG features. In particular, EEGFuseNet is established as an optimal unsupervised fusion model with promising cross-subject emotion recognition performance. It proves EEGFuseNet is capable of characterizing and fusing deep features that imply comparative cortical dynamic significance corresponding to the changing of different emotion states, and also demonstrates the possibility of realizing EEG based cross-subject emotion recognition in a pure unsupervised manner.

A Grouped Dynamic EEG Channel Selection Method for Emotion Recognition

2021 ◽  
Author(s):  
Liying Yang ◽  
Si Chao ◽  
Qingyang Zhang ◽  
Pei Ni ◽  
Dunhui Liu
Keyword(s):  
Emotion Recognition ◽  
Channel Selection ◽  
Selection Method
Sign in / Sign up

Export Citation Format

Share Document