Journal ArticleDOI
Feature extraction from EEGs associated with emotions
TLDR
This new technique has a wide variety of applications in both medical and non-medical areas, and the technology suggests the possibility of direct control of systems by the human emotional state.Abstract:
The state of mind of a person is supported by the brain activity, and hence features of the state of mind appear in the scalp potentials, as seen on an electroencephalogram (EEG). The EEG features have been extracted into a set of 135 state variables of cross-correlation coefficients of EEGs collected with ten scalp electrodes in the θ, α, and β frequency bands corresponding toanger, sadness, joy, andrelaxation. An emotion matrix is defined which transforms the set of 135 state variables into a four-element emotion vector of which the components are indexes corresponding to the four elementary emotional states. The maximum time resolution of the emotion analysis is 0.64s and it is done in real time. This new technique has a wide variety of applications in both medical and non-medical areas, and the technology suggests the possibility of direct control of systems by the human emotional state.read more
Citations
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Journal ArticleDOI
Feature Extraction and Selection for Emotion Recognition from EEG
TL;DR: This work reviews feature extraction methods for emotion recognition from EEG based on 33 studies, and results suggest preference to locations over parietal and centro-parietal lobes.
Journal ArticleDOI
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
TL;DR: The proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels via training a neural network so as to benefit for more discriminative EEG feature extraction.
Proceedings ArticleDOI
Emotion recognition using brain activity
TL;DR: This system was designed using prior knowledge from other research, and is meant to assess the quality of emotion recognition using EEG signals in practice, and found that the EEG signals contained enough information to separate five different classes on both the valence and arousal dimension.
Journal ArticleDOI
Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis
TL;DR: Detailed experiments on EEG-based emotion recognition based on the SJTU emotion EEG dataset and experimental results demonstrate that the proposed GSCCA method would outperform the state-of-the-art EEG- based emotion recognition approaches.
Journal ArticleDOI
EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
TL;DR: A novel emotion recognition method based on a novel deep learning model (ERDL) which fuses graph convolutional neural network (GCNN) and long-short term memories neural networks (LSTM) and achieves better classification results than state-of-the-art methods.
References
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Journal ArticleDOI
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Journal ArticleDOI
Hemispheric lateralization of functions related to emotion
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