An Emotion Recognition Embedded System using a Lightweight Deep Learning Model

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Bazargani Mehdi; Tahmasebi Amir; Yazdchi Mohammadreza; Baharlouei Zahra

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Journal Paper

Paper Information

Journal: JOURNAL OF MEDICAL SIGNALS AND SENSORS (JMSS) Year:2023 | Volume:13 | Issue:4 Page(s): 272-279

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Information Journal Paper

Title

An Emotion Recognition Embedded System using a Lightweight Deep Learning Model

Author(s)

Bazargani Mehdi | Tahmasebi Amir | Yazdchi Mohammadreza | Baharlouei Zahra | Issue Writer Certificate

Keywords

Convolutional neural network

electroencephalography

embedded system

emotion recognition

Abstract

Background: Diagnosing emotional states would improve human-computer interaction (HCI) systems to be more effective in practice. Correlations between electroencephalography (EEG) signals and emotions have been shown in various research,therefore, EEG signal-based methods are the most accurate and informative. Methods: In this study, three Convolutional neural network (CNN) models, EEGNet, ShallowConvNet and DeepConvNet, which are appropriate for processing EEG signals, are applied to diagnose emotions. We use baseline removal preprocessing to improve classification accuracy. Each network is assessed in two setting ways: subject-dependent and subject-independent. We improve the selected CNN model to be lightweight and implementable on a Raspberry Pi processor. The emotional states are recognized for every three-second epoch of received signals on the embedded system, which can be applied in real-time usage in practice. Results: Average classification accuracies of 99. 10% in the valence and 99. 20% in the arousal for subject-dependent and 90. 76% in the valence and 90. 94% in the arousal for subject independent were achieved on the well-known DEAP dataset. Conclusion: Comparison of the results with the related works shows that a highly accurate and implementable model has been achieved for practice.

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