Journal ArticleDOI
EEG-Based Emotion Recognition in Music Listening
Yuan-Pin Lin,Chi-Hong Wang,Tzyy-Ping Jung,Tien-Lin Wu,Shyh-Kang Jeng,Jeng Ren Duann,Jyh-Horng Chen +6 more
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TLDR
This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening to identify 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics duringMusic listening.Abstract:
Ongoing brain activity can be recorded as electroen-cephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% ± 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.read more
Citations
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Journal ArticleDOI
Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks
Wei-Long Zheng,Bao-Liang Lu +1 more
TL;DR: The experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals, and the performance of deep models with shallow models is compared.
Journal ArticleDOI
Feature Extraction and Selection for Emotion Recognition from EEG
TL;DR: This work reviews feature extraction methods for emotion recognition from EEG based on 33 studies, and results suggest preference to locations over parietal and centro-parietal lobes.
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
EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks
TL;DR: 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.
Journal ArticleDOI
Emotional state classification from EEG data using machine learning approach
TL;DR: From experimental results, it is found that power spectrum feature is superior to other two kinds of features; a linear dynamic system based feature smoothing method can significantly improve emotion classification accuracy; and the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning.
References
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Combining SVMs with Various Feature Selection Strategies
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