Emotional State Recognition with EEG Signals Using Subject Independent Approach

2018 ◽  
pp. 117-124 ◽  
Author(s):  
Pallavi Pandey ◽  
K. R. Seeja
Keyword(s):  
Emotional State ◽  
Eeg Signals ◽  
State Recognition

Reversed Correlation-Based Pairwised EEG Channel Selection in Emotional State Recognition

2021 ◽  
pp. 528-541
Author(s):  
Aleksandra Dura ◽  
Agnieszka Wosiak ◽  
Bartłomiej Stasiak ◽  
Adam Wojciechowski ◽  
Jan Rogowski
Keyword(s):  
Emotional State ◽  
Channel Selection ◽  
State Recognition

Emotional State Recognition Based on Functional Near-Infrared Spectroscopy

2016 ◽  
Vol 36 (3) ◽  
pp. 0317002
Author(s):  
姜劲 Jiang Jin ◽  
焦学军 Jiao Xuejun ◽  
潘津津 Pan Jinjin ◽  
张朕 Zhang Zhen ◽  
曹勇 Cao Yong ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Emotional State ◽  
Functional Near Infrared Spectroscopy ◽  
State Recognition

EEG-based emotion recognition with deep convolutional neural networks

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mehmet Akif Ozdemir ◽  
Murside Degirmenci ◽  
Elif Izci ◽  
Aydin Akan
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Convolutional Neural Networks ◽  
Spatial Information ◽  
Emotional State ◽  
Human Interaction ◽  
Emotional States ◽  
Eeg Signals ◽  
Deep Convolutional Neural Networks ◽  
Spectral Topology

AbstractThe emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed to communicate brain signals with external systems and make predictions over emotional states. This paper proposes a novel method for emotion recognition based on deep convolutional neural networks (CNNs) that are used to classify Valence, Arousal, Dominance, and Liking emotional states. Hence, a novel approach is proposed for emotion recognition with time series of multi-channel EEG signals from a Database for Emotion Analysis and Using Physiological Signals (DEAP). We propose a new approach to emotional state estimation utilizing CNN-based classification of multi-spectral topology images obtained from EEG signals. In contrast to most of the EEG-based approaches that eliminate spatial information of EEG signals, converting EEG signals into a sequence of multi-spectral topology images, temporal, spectral, and spatial information of EEG signals are preserved. The deep recurrent convolutional network is trained to learn important representations from a sequence of three-channel topographical images. We have achieved test accuracy of 90.62% for negative and positive Valence, 86.13% for high and low Arousal, 88.48% for high and low Dominance, and finally 86.23% for like–unlike. The evaluations of this method on emotion recognition problem revealed significant improvements in the classification accuracy when compared with other studies using deep neural networks (DNNs) and one-dimensional CNNs.

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