Journal ArticleDOI
EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm
Hyun Joong Yoon,Seong Youb Chung +1 more
TLDR
A probabilistic classifier based on Bayes' theorem and a supervised learning using a perceptron convergence algorithm to address the emotion recognition problem from electroencephalogram signals.About:
This article is published in Computers in Biology and Medicine.The article was published on 2013-12-01. It has received 150 citations till now. The article focuses on the topics: Perceptron & Supervised learning.read more
Citations
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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
Emotion Recognition based on EEG using LSTM Recurrent Neural Network
TL;DR: 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.
Journal ArticleDOI
Recognition of emotions using multimodal physiological signals and an ensemble deep learning model
TL;DR: The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances.
Journal ArticleDOI
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
TL;DR: The emotion recognition methods based on multi-channel EEG signals as well as multi-modal physiological signals are reviewed and the correlation between different brain areas and emotions is discussed.
References
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
TL;DR: In this article, the maximal statistical dependency criterion based on mutual information (mRMR) was proposed to select good features according to the maximal dependency condition. But the problem of feature selection is not solved by directly implementing mRMR.
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI
An argument for basic emotions
TL;DR: This work has shown that not only the intensity of an emotion but also its direction may vary greatly both in the amygdala and in the brain during the course of emotion regulation.