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首页> 外文会议>International Conference on Image, Video Processing and Artificial Intelligence >A study of the effect of training sample size on a pre-trained model of CRNN EEG emotion recognition
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A study of the effect of training sample size on a pre-trained model of CRNN EEG emotion recognition

机译：训练样本大小对CRNN EEG情绪识别预先训练模型的影响

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To address the time-consuming feature extraction and model training in the process of EEG emotion recognition, this paper proposes a method to rapidly train deep learning models for EEG emotion recognition with high accuracy and excellent performance. The DEAP EEG data set is used to quickly train and fit the deep learning model, so as to establish a new pre-trained model for EEG emotion recognition. In addition, it was found that the best training effect was achieved using a sample with a ratio of 25%, and the other test data could quickly fine-tune the original model. The experimental results proved the effectiveness of the method, and the accuracy of the pre-trained model could reach the highest 93.72% in the Valence emotion dimension.
机译：为了解决EEG情绪识别过程中耗时的特征提取和模型培训，提出了一种以高精度和优异的性能迅速培训深度学习模型的方法。 DEAP EEG数据集用于快速培训并符合深度学习模型，以便为脑电图识别建立一个新的预先训练的模型。此外，发现使用比例为25％的样品实现了最佳培训效果，而另一个测试数据可以快速微调原始模型。实验结果证明了该方法的有效性，预先训练模型的准确性可以在价情绪维度中达到最高的93.72％。

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