• Corpus ID: 238744094

Transformers for EEG Emotion Recognition

  title={Transformers for EEG Emotion Recognition},
  author={Jiyao Liu and Li Zhang and Hao Wu and Huan Zhao},
  • Jiyao Liu, Li Zhang, +1 author Huan Zhao
  • Published 13 October 2021
  • Computer Science
  • ArXiv
Electroencephalogram (EEG) can objectively reflect emotional state and changes. However, the transmission mechanism of EEG in the brain and its internal relationship with emotion are still ambiguous to human beings. This paper presents a novel approach to EEG emotion recognition built exclusively on self-attention over the spectrum, space, and time dimensions to explore the contribution of different EEG electrodes and temporal slices to specific emotional states. Our method, named EEG emotion… 
1 Citations

Figures and Tables from this paper

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
A computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals using the proposed CNN–RNN method for Bonn and Freiburg datasets with an accuracy of 99.71% and 99.13%, respectively.


Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network
This research used Deep Neural Network to address EEG-based emotion recognition, and adapted DNN to identify human emotions of a given EEG signal (DEAP dataset) from power spectral density (PSD) and frontal asymmetry features.
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 85.11% is achieved for four emotions.
Sparse Graphic Attention LSTM for EEG Emotion Recognition
A novel multichannel EEG emotion recognition method based on sparse graphic attention long short-term memory (SGA-LSTM) is proposed, which is superior to the state-of-the-art methods.
EEG-Based Emotion Classification Using Long Short-Term Memory Network with Attention Mechanism
A long short-term memory network is proposed to consider changes in emotion over time and apply an attention mechanism to assign weights to the emotional states appearing at specific moments based on the peak–end rule in psychology.
Emotion Recognition based on EEG using LSTM Recurrent Neural Network
A deep learning method is proposed to recognize emotion from raw EEG signals using Long-Short Term Memory (LSTM) and the dense layer classifies these features into low/high arousal, valence, and liking.
Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition
Benefiting from the strong representational learning capacity in the two-dimensional space, HCNN is efficient in emotion recognition especially on Beta and Gamma waves.
A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition
  • Y. Li, Lei Wang, +5 authors Tengfei Song
  • Computer Science, Biology
    IEEE Transactions on Cognitive and Developmental Systems
  • 2021
The effectiveness and advantage of the proposed BiHDM model in solving the EEG emotion recognition problem are demonstrated and the important brain areas in emotion expression are investigated.
EEG-based Intention Recognition from Spatio-Temporal Representations via Cascade and Parallel Convolutional Recurrent Neural Networks
Both cascade and parallel convolutional recurrent neural network models for precisely identifying human intended movements by effectively learning compositional spatio-temporal representations of raw EEG streams are introduced.
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
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.
Differential entropy feature for EEG-based emotion classification
EEG-based emotion recognition has been studied for a long time. In this paper, a new effective EEG feature named differential entropy is proposed to represent the characteristics associated with