EEG-Based Emotion Recognition using 3D Convolutional Neural Networks

Elham S. Salama · Reda A. El-Khoribi · Mahmoud E. Shoman · Mohamed A. Wahby

251 citations0/0 files storedInternational Journal of Advanced Computer Science and Applications

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Gaze Tracking and Assistive Technology

Abstract

Emotion recognition is a crucial problem in Human-Computer Interaction (HCI). Various techniques were applied to enhance the robustness of the emotion recognition systems using electroencephalogram (EEG) signals especially the problem of spatiotemporal features learning. In this paper, a novel EEG-based emotion recognition approach is proposed. In this approach, the use of the 3-Dimensional Convolutional Neural Networks (3D-CNN) is investigated using a multi-channel EEG data for emotion recognition. A data augmentation phase is developed to enhance the performance of the proposed 3D-CNN approach. And, a 3D data representation is formulated from the multi-channel EEG signals, which is used as data input for the proposed 3D-CNN model. Extensive experimental works are conducted using the DEAP (Dataset of Emotion Analysis using the EEG and Physiological and Video Signals) data. It is found that the proposed method is able to achieve recognition accuracies 87.44% and 88.49% for valence and arousal classes respectively, which is outperforming the state of the art methods.

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References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 2008Emotion recognition based on physiological changes in music listening 2017Emotion Recognition based on EEG using LSTM Recurrent Neural Network 2018Spatial–Temporal Recurrent Neural Network for Emotion Recognition 2015Improving BCI-based emotion recognition by combining EEG feature selection and kernel classifiers 2016Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network

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2020Emotion Recognition From Multi-Channel EEG via Deep Forest 2019Accurate EEG-Based Emotion Recognition on Combined Features Using Deep Convolutional Neural Networks 2019A Hierarchical Bidirectional GRU Model With Attention for EEG-Based Emotion Classification 2019A Multi-Column CNN Model for Emotion Recognition from EEG Signals 2021Differences first in asymmetric brain: A bi-hemisphere discrepancy convolutional neural network for EEG emotion recognition 2022GANSER: A Self-Supervised Data Augmentation Framework for EEG-Based Emotion Recognition 2022

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