ArticleFrontiers in neuroscience2020
Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Cross-subject generalization for EEG emotion recognition: a review of methods, challenges, and future trends.Frontiers in computational neuroscience · 2026Review
- Review
- Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks.Wellcome open research · 2024Article
- A Review of Recurrent Neural Network-Based Methods in Computational Physiology.IEEE transactions on neural networks and learning systems · 2023Review
- Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients.Healthcare (Basel, Switzerland) · 2023Article
- The increasing instance of negative emotion reduce the performance of emotion recognition.Frontiers in human neuroscience · 2023Article
- Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey.Sensors (Basel, Switzerland) · 2022Review
- An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications.Entropy (Basel, Switzerland) · 2021Review
- Low-Dimensional Dynamics of Brain Activity Associated with Manual Acupuncture in Healthy Subjects.Sensors (Basel, Switzerland) · 2021Article
- Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks.Sensors (Basel, Switzerland) · 2021Article
- Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization.Frontiers in neuroscience · 2021Article
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9 authors.
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Abstract
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.
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