Book ChapterDOI
Detecting Different Emotional States of Human Brain Using Bio-potential Signals
Prithwijit Mukherjee,Anisha Halder Roy +1 more
- pp 94-101
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TLDR
In this paper, a k-nearest neighbor (KNN) classifier model was proposed for predicting the emotional state of a person using EEG signals of frontal lobe, pulse rate and SpO2.Abstract:
The essence of this paper is to design a mechanism for detecting emotional state of person using different bio-potential signals like electroencephalogram (EEG) signals of frontal lobe, pulse rate and SpO2. We record EEG signals of Fp1, Fp2, F3 and F4 electrodes, pulse rate and SpO2 of thirty subjects and extract twenty-two features from the recorded bio-potential signals. We design a k-nearest neighbor (KNN) classifier model for predicting the emotional state of a person. The designed KNN classifier model is trained with the extracted feature values of the thirty subjects. We again record the same bio-potential signals of ten new subjects and extract features. These extracted feature values are used for validating the performance of the trained KNN classifier model. The obtained overall efficiency of our designed emotion detection mechanism is 95.4%.read more
References
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Journal ArticleDOI
EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation
TL;DR: A deep learning network (DLN) is proposed to discover unknown feature correlation between input signals that is crucial for the learning task and provides better performance compared to SVM and naive Bayes classifiers.
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
Emotion recognition from facial EMG signals using higher order statistics and principal component analysis
TL;DR: The results of this work indicate an improved mean emotion recognition rate of 69.5% from this proposed methodology to recognize six emotional states.
Book ChapterDOI
Analysis of Facial EMG Signal for Emotion Recognition Using Wavelet Packet Transform and SVM
TL;DR: This paper proposed techniques for recognizing three different facial expressions such as happiness, anger, and disgust, which gives classification accuracy 91.66% on 12 subjects.
Proceedings ArticleDOI
Video-based Emotion Recognition Using Multi-dichotomy RNN-DNN
TL;DR: A multi-dichotomy Recurrent Neural Network-Deep Neural Network (RNN-DNN) based framework for emotion classification is explored, which aggregate VGG Face-based face features from a same video to a global featurerepresentation via its RNN layer, and further map the global feature representation to an emotional category using its dichotomy DNN layers.