Improving EEG-Based Emotion Classification Using Conditional Transfer Learning论文解读
文章问题提出：情感分类需要每个人提供大量的校准数据但那是费时费力的，怎么才能不通过增加样本数据下提高检测的准确度？ 文章方案：通过条件迁移学习来解决那些样本分类不好的个体，即从源域中选取与目标域相似的样本，组成新的源域从而提高检测的准曲率。 dataset: Oscar soundtrack EEG dataset.26个个体每个人有16段的30-s 30-channel 脑电实验和自我评价lab
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