A novel convolution bi-directional gated recurrent unit neural network for emotion recognition in multichannel electroencephalogram signals.

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 (𝐸𝑛𝑆𝐸→) and energy and differential entropy (𝐸𝑛𝐷𝐸→) 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).