In Technology and health care : official journal of the European Society for Engineering and Medicine
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).
Abgeena Garg
2022-Dec-29
BiGRU, CNN, EEG, emotion recognition, hybrid deep learning
