Robust cross-subject emotion recognition based on multichannel EEG has always been hard work. In this work, we hypothesize that there exist default brain variables across subjects in emotional processes. Hence, the states of the latent variables that relate to emotional processing must contribute to building robust recognition models. Specifically, we propose to utilize an unsupervised deep generative model (e.g., variational autoencoder) to determine the latent factors from the multichannel EEG. Through a sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED) and compared with traditional matrix factorization-based (ICA) and autoencoder-based approaches. Experimental results demonstrate that autoencoder-like neural networks are suitable for unsupervised EEG modeling, and our proposed emotion recognition framework achieves an inspiring performance. As far as we know, it is the first work that introduces variational autoencoder into multichannel EEG decoding for emotion recognition. We think the approach proposed in this work is not only feasible in emotion recognition but also promising in diagnosing depression, Alzheimer's disease, mild cognitive impairment, etc., whose specific latent processes may be altered or aberrant compared with the normal healthy control.

Chun-Yang(牛春洋) a) , Qi Hong(齐 宏) a) † , Huang Xing(黄 兴) a) , Ruan Li-Ming(阮立明) a) , Wang Wei(王 伟) b) , and Tan He-Ping(谈和平) a)

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An inverse estimation method and corresponding measurement system are developed to measure the apparent spectral directional emissivities of semitransparent materials. The normal spectral emissivity and transmissivity serve as input for the inverse analysis. Consequently, the refractive index and absorption coefficient of the semitransparent material could be retrieved by using the pseudo source adding method as the forward method and the stochastic particle swarm optimization algorithm as the inverse method. Finally, the arbitrary apparent spectral directional emissivity of semitransparent material is estimated by using the pseudo source adding method given the retrieval refractive index and absorption coefficient. The present system has the advantage of a simple experimental structure, high accuracy, and excellent capability to measure the emissivity in an arbitrary direction. Furthermore, the apparent spectral directional emissivity of sapphire at 773 K is measured by using this system in a spectral range of 3 µm-12 µm and a viewing range of 0 • -90 • . The present method paves the way for a new directional spectral emissivity measurement strategy.