Evaluating Classifiers for Emotion Recognition Using EEG

@inproceedings{Sohaib2013EvaluatingCF,
  title={Evaluating Classifiers for Emotion Recognition Using EEG},
  author={Ahmad Tauseef Sohaib and Shahnawaz Qureshi and Johan Hagelb{\"a}ck and Olle Hilborn and Petar Jercic},
  booktitle={HCI},
  year={2013}
}
There are several ways of recording psychophysiology data from humans, for example Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG. [] Key Method K-Nearest Neighbor (KNN), Bayesian Network (BN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are some machine learning techniques that previously have been used to classify EEG data in various experiments. Five different machine learning techniques were evaluated in this paper…
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The results showed that it is difficult to train a classifier to be accurate over the high number of emotions but SVM with proposed features were reasonably accurate over smaller emotion group identifying the emotional states with an accuracy up to 70%.
AsEmo: Automatic Approach for EEG-Based Multiple Emotional State Identification
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Experimental results on real-world EEG emotion recognition tasks demonstrate that AsEmo outperforms other state-of-the-art methods with a 2–8% improvement in terms of classification accuracy.
Review of the emotional feature extraction and classification using EEG signals
Classification of EEG-based emotion for BCI applications
TLDR
Using appropriate feature for extraction emotional state such as Discrete Wavelet Transform (DWT) and suitable learner such as Aftificial Neural Network (ANN), recognizer system can be accurately recognized and classify six emotional states such as fear, sad, frustrated, happy, pleasant and satisfied from inner emotion EEG signals.
Emotion recognition using EEG signal and deep learning approach
TLDR
This thesis presents an approach to classify human emotions using EEG signal by Convolutional Neural Network (CNN), and uses the Dataset for Emotion Analysis using Physiological signals (DEAP) dataset to transform the EEG signal from time domain to frequency domain and extract the features to classify the emotions.
Deep learninig of EEG signals for emotion recognition
TLDR
A deep learning algorithm is proposed to simultaneously learn the features and classify the emotions of EEG signals to achieve better recognition accuracy than conventional algorithms.
Combined analysis of GSR and EEG signals for emotion recognition
TLDR
The results of research related to the detection of emotions using combined analysis of galvanic skin response (GSR) and electroencephalographic (EEG) signals and two classifiers were implemented: support vector machine and k-nearest neighbors (k-NN).
Emotion recognition from EEG brain signals based on particle swarm optimization and genetic search
  • R. M. Mehmood, H. Lee
  • Computer Science
    2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
  • 2016
TLDR
Emotion recognition accuracy had shown the possibility of classification of EEG brain activity from human brain signals using electroencephalography (EEG).
EEG-based Classification of the Intensity of Emotional Responses
TLDR
The proposed model excels in classifying emotional intensity and provides superior performance compared to the state-of-the-art emotion classification systems.
A Novel Two-tier Classifier based on K-Nearest Neighbour and Neural Network Classifier for Emotion Recognition using EEG Signals
TLDR
A novel approach to classify human emotions using a two-tier classifier which is a combination of both K-Nearest Neighbour (K-NN) and Neural Network (NN) classifier is presented.
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