Affiliations: Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
Correspondence:
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Corresponding author: Shruti Garg, Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India. %****โฃthc__1-thc220458_temp.texโฃLineโฃ50โฃ**** E-mail: gshruti@bitmesra.ac.in.
Abstract: BACKGROUND: Recognising emotions in humans is a great challenge in the present era and has several applications under affective computing. Deep learning (DL) found a success tool for predict for emotions in different modalities. OBJECTIVE: To predict 3D emotions with high accuracy in multichannel physiological signals, i.e. electroencephalogram (EEG). METHODS: A hybrid DL model consist of CNN and GRU is proposed in this work for emotion recognition in EEG recordings. A convolution neural network (CNN) has the capability of learning abstract representation, whereas gated recurrent units (GRU) have the capability of exploring temporal correlation. A bi-directional variation of GRU is used here to learn features in both directions. Discrete and dimensional emotion indices are recognised in two publicly available datasets namely SEED and DREAMER, respectively. A fused feature of energy and Shannon entropy (๐ธ๐๐๐ธโ) and energy and differential entropy (๐ธ๐๐ท๐ธโ) features are fed to the proposed classifier to improve the efficiency of the model. RESULTS: The performance of the presented model is measured in terms of average accuracy, which is obtained as 86.9% and 93.9% for SEED and DREAMER datasets, respectively. CONCLUSION: The proposed convolution bi-directional gated recurrent unit neural network (CNN-BiGRU) model outperforms most of the state-of-the-art and competitive hybrid DL models, which indicates the effectiveness of emotion recognition using EEG signals and provides a scientific base for the implementation of human-computer interaction (HCI).
Keywords: EEG, hybrid deep learning, CNN, BiGRU, emotion recognition
