A study of the effect of training sample size on a pre-trained model of CRNN EEG emotion recognition

Jinping Qiu; Jian Zhao; Xiankai Cheng

doi:10.1117/12.2583588

10 November 2020 A study of the effect of training sample size on a pre-trained model of CRNN EEG emotion recognition

Jinping Qiu, Jian Zhao, Xiankai Cheng

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 1158420 (2020) https://doi.org/10.1117/12.2583588
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

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.

Citation Download Citation

Jinping Qiu, Jian Zhao, and Xiankai Cheng "A study of the effect of training sample size on a pre-trained model of CRNN EEG emotion recognition", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 1158420 (10 November 2020); https://doi.org/10.1117/12.2583588

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