Bispectral Analysis of EEG for Emotion Recognition
Nitin Kumar,Kaushikee Khaund,Shyamanta M. Hazarika +2 more
- Vol. 84, pp 31-35
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TLDR
In this article, derived features of bispectrum for quantification of emotions using a Valence-Arousal emotion model were explored and a feature vector was obtained through backward sequential search.Abstract:
Emotion recognition from electroencephalogram (EEG) signals is one of the most challenging tasks. Bispectral analysis offers a way of gaining phase information by detecting phase relationships between frequency components and characterizing the non- Gaussian information contained in the EEG signals. In this paper, we explore derived features of bispectrum for quantification of emotions using a Valence-Arousal emotion model; and arrive at a feature vector through backward sequential search. Cross- validated accuracies of 64.84% for Low/High Arousal classification and 61.17% for Low/High Valence were obtained on the DEAP data set based on the proposed features; comparable to classification accuracies reported in the literature.read more
Citations
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Journal ArticleDOI
Emotions Recognition Using EEG Signals: A Survey
TL;DR: A survey of the neurophysiological research performed from 2009 to 2016 is presented, providing a comprehensive overview of the existing works in emotion recognition using EEG signals, and a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results.
Journal ArticleDOI
SAE+LSTM: A New Framework for Emotion Recognition From Multi-Channel EEG.
TL;DR: This paper presents a novel framework for emotion recognition using multi-channel electroencephalogram (EEG), which considerably decomposes the EEG source signals from the collected EEG signals and improves classification accuracy by using the context correlations of the EEG feature sequences.
Journal ArticleDOI
Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks
TL;DR: The experimental results showed that the deep CNN models which require no feature engineering achieved the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension.
Journal ArticleDOI
A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia.
TL;DR: A novel multi-modal Machine Learning (ML) based approach is proposed to integrate EEG engineered features for automatic classification of brain states and results show that the Multi-Layer Perceptron (MLP) classifier outperforms all other models, specifically, the Autoencoder, Logistic Regression (LR) and Support Vector Machine (SVM).
Journal ArticleDOI
Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.
TL;DR: This paper provided better frequency bands and channels reference for emotion recognition based on EEG and found the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands.
References
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A Multimodal Database for Affect Recognition and Implicit Tagging
TL;DR: Results show the potential uses of the recorded modalities and the significance of the emotion elicitation protocol and single modality and modality fusion results for both emotion recognition and implicit tagging experiments are reported.
Book ChapterDOI
EEG-based emotion recognition using frequency domain features and support vector machines
TL;DR: Experimental results indicate that an average test accuracy of 66.51% for classifying four emotion states can be obtained by using frequency domain features and support vector machines.
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Real-time EEG-based happiness detection system.
TL;DR: Real-time EEG signal is used to classify happy and unhappy emotions elicited by pictures and classical music, using PSD as a feature and SVM as a classifier to implement happiness detection system using only one pair of channels.
Journal ArticleDOI
Statistical methods for investigating phase relations in stationary stochastic processes
TL;DR: In this article, the authors discuss proposals for testing the presence of phase relations and for extracting them quantitatively by means of numerical bispectrum analysis, and derive their statistical properties and compare their relative merits.
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