Self-supervised Group Meiosis Contrastive Learning for EEG …
2022年7月12日 · To address the issue, this paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel genetics-inspired data augmentation method, named Meiosis, is …
基于self-supervised learning - 知乎 - 知乎专栏
2023年3月18日 · A Self-supervised Group Meiosis Contrastive learning framework (SGMC) 是一篇来自于TAC的一篇论文，主要将来自相同内容的脑电信号对齐，然后进行微调，在这里乜有one-subject-out的验证试验。这篇论文的目的是…
kanhaoning/Self-supervised-group-meiosis-contrastive-learning …
There is a self-supervised learning framework to realize EEG-based emotion classification on DEAP and SEED dataset. Article title： Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion Recognition Article link： https://arxiv.org/abs/2208.00877
Self-supervised group meiosis contrastive learning for EEG-based ...
2023年9月5日 · To address this issue, this paper proposes a self-supervised group meiosis contrastive learning (SGMC) framework for EEG-based emotion recognition. First, to reduce the dependence of emotion labels, SGMC introduces a contrastive learning task according to the alignment of video clips based on the similar EEG response across subjects.
论文阅读：Self-supervised Group Meiosis Contrastive Learning …
2024年5月16日 · 在神经科学领域中认为，情绪脑电信号中具有可利用的一致性，也就是说，不同被试在接收到相同的情感刺激的时，会产生一致的或相似的脑电信号的特征，而这些与Arousal、Valence、Preference等有关的相似的脑电信. 文章链接： https://link.springer.com/article/10.1007/s10489-023-04971-0Code： GitHub - kanhaoning/Self-supervised-group-meiosis-contrastive-learning-for-EEG-based-emotion-recognition1 …
Meiosis Contrastive Learning (SGMC) framework to exploit such stimuli labels for emotion recognition. The SGMC adopts a genetic inspired data augmentation method Meiosis. It achieves augmenting a group of samples sharing the same stimuli label to generate two augmented groups by pairing, cross exchanging, and separating.
Self-supervised Group Meiosis Contrastive Learning for EEG
To address the issue, this paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel genetics-inspired data augmentation method, named Meiosis, is developed.
Self-supervised group meiosis contrastive learning for EEG-based ...
To address this issue, this paper proposes a self-supervised group meiosis contrastive learning (SGMC) framework for EEG-based emotion recognition. First, to reduce the dependence of emotion labels, SGMC introduces a contrastive learning task according to the alignment of video clips based on the similar EEG response across subjects.
Self-supervised Group Meiosis Contrastive Learning for EEG …
This paper proposed a Self-supervised Group Meiosis Contrastive Learning (SGMC) framework to exploit such stimuli labels for emotion recognition. The SGMC adopts a genetic inspired data augmentation method Meiosis.
2022年8月2日 · paper proposes a Self-supervised Group Meiosis Contrastive learning framework (SGMC) based on the stimuli consistent EEG signals in human being. In the SGMC, a novel genetics-inspired data augmentation method, named Meiosis, is developed. It takes advantage of the alignment of stimuli among the EEG