The multiscale 3D convolutional network for emotion recognition based on electroencephalogram
Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion re...
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Published in | Frontiers in neuroscience Vol. 16; p. 872311 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Lausanne
Frontiers Research Foundation
15.08.2022
Frontiers Media S.A |
Subjects | |
Online Access | Get full text |
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Summary: | Emotion recognition based on EEG (electroencephalogram) has become a research hotspot in the field of brain-computer interfaces (BCI). Compared with traditional machine learning, the convolutional neural network model has substantial advantages in automatic feature extraction in EEG-based emotion recognition. Motivated by the studies that multiple smaller scale kernels could increase non-linear expression than a larger scale, we propose a 3D convolutional neural network model with multiscale convolutional kernels to recognize emotional states based on EEG signals. We select more suitable time window data to carry out the emotion recognition of four classes (low valence vs. low arousal, low valence vs. high arousal, high valence vs. low arousal, and high valence vs. high arousal). The results using EEG signals in the DEAP and SEED-IV datasets show accuracies for our proposed emotion recognition network model (ERN) of 95.67 and 89.55%, respectively. The experimental results demonstrate that the proposed approach is potentially useful for enhancing emotional experience in BCI. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Neuroprosthetics, a section of the journal Frontiers in Neuroscience Edited by: Varun Bajaj, PDPM Indian Institute of Information Technology, Design and Manufacturing, India Reviewed by: Chang Li, Hefei University of Technology, China; Yu Zhang, Lehigh University, United States |
ISSN: | 1662-453X 1662-4548 1662-453X |
DOI: | 10.3389/fnins.2022.872311 |