Leveraging Sparse Coding for EEG Based Emotion Recognition in Shooting

Emotion recognition in shooting is of great importance for improving athletes' training methods. However, there is no open and high confident electroencephalography (EEG) dataset about shooting due to the difficulty of data acquisition, which made it a challenge for related studies. In this paper, we collected EEG of novice shooters and high-level shooters in different emotion states, and established two shooting datasets. Furthermore, instead of adopting the common convolutional neural network, we are the first to leverage sparse coding for EEG based emotion recognition in shooting process. Our proposed method can effectively solve the problem of low accuracy caused by data with low signal-noise ratio and small training set. The experimental results demonstrate that our method outperforms other representative deep learning based methods.