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Open AccessArticle

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System

Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
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Sensors 2018, 18(5), 1383; https://doi.org/10.3390/s18051383
Received: 20 March 2018 / Revised: 25 April 2018 / Accepted: 26 April 2018 / Published: 30 April 2018
The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models. View Full-Text
Keywords: EEG; GSR; emotion recognition; deep learning; convolution neural networks; pattern recognition; hybrid neural network EEG; GSR; emotion recognition; deep learning; convolution neural networks; pattern recognition; hybrid neural network
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Kwon, Y.-H.; Shin, S.-B.; Kim, S.-D. Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System. Sensors 2018, 18, 1383.

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