Brain lateralisation feature extraction and ant colony optimisation-bidirectional LSTM network model for emotion recognition

The human inner perception is the core technology for human-robot interactions. Emotion recognition has been established as a representative study for recognising the internal state of human beings in addition to research regarding intention recognition. In the study of emotion recognition using EEG signals, emotion evaluation is categorised into two methods: discrete emotion and continuous emotion. This study proposes an ant colony optimisation-bidirectional LSTM network model. Unlike other LSTM network models, this model improves performance by applying more weight values that are valid for emotion recognition in the current LSTM cell state using past and future biosignal information and combining ACO to find the optimal combination of emotion recognition features among many features. Furthermore, it extracts valid features using peripheral nervous system signals PPG, GSR, and EOG as well as the central nervous system signals EEG, simultaneously. The authors reinforce feature performance by adding brain lateralisation for emotion recognition. Emotion label data were recorded by performing annotation labelling, in which the values are between −100 and 100 in the arousal-valence domain. The experimental result shows that the ACO-bidirectional LSTM model using brain lateralisation in the MAHNOB-HCI, DEAP, MERTI-Apps database yields the best valence performance, RMSE of 0.0442, 0.0523, and 0.0568, respectively.