SSVEP-Based Emotion Recognition for IoT via Multiobjective Neural Architecture Search

Emotion recognition is one of the significant research areas and applications of electroencephalography (EEG)-based brain–computer interface (BCI), which is widely concerned in the Internet of Things (IoT) framework. Steady-state visual evoked potential (SSVEP) is frequently used in EEG-based BCI (EEG-BCI) due to its high signal-to-noise ratio and short response time. However, there are few related studies and applications on the emotional features of SSVEP. In this article, we build an SSVEP-based BCI (SSVEP-BCI) for affective computing in the IoT system and utilize neural architecture search (NAS) to analyze the emotional information of SSVEP signals. Our proposed NAS is capable of optimizing the classification performance and model size of the deep neural networks simultaneously, thus enhancing the sentiment analysis accuracy for SSVEP and improving the response speed of the actual BCI system for IoT implementation. Furthermore, we employ network morphism and Bayesian optimization in the offspring generation algorithm of the NAS framework to apply knowledge inheritance and performance estimation for the child models, respectively. The experimental results show that the proposed NAS outperforms the baseline models in terms of accuracy and model size for valence and arousal dimensions. Moreover, the offspring generation algorithm is able to promote the search efficiency of NAS.