基于三段式特征选择策略的脑电情感识别算法SEE

SEE:sense EEG-based emotion algorithm via three-step feature selection strategy

情感可以通过脑电信号中的隐藏模式来识别,基于众多的脑电通道提取的脑电特征数量庞大,使情感识别任务非常复杂。针对上述问题,提出了基于三段式特征选择策略的脑电情感识别算法(SEE)。本文从时域、频域、空间域系统地提取脑电特征,基于提取的脑电特征集合,首先通过t检验去除类间无显著差异的特征,再使用递归特征消除策略进行目标相关的特征选择,最后通过顺序后向特征选择策略确定最终的特征子集用于情感识别。实验结果表明:相较于其他方法,本文构建的模型有更好的情感识别能力。与现有的特征选择算法相比,SEE具有较低的时间复杂度并且能筛选出较优的特征子集。此外,分析了与情感相关的脑电通道和频带,实验结果体现了一定的生理学意义,为开发情绪靶向的脑电设备提供可能。

Emotions can be recognized through the hidden patterns in the EEG signals. The large number of EEG features extracted based on numerous EEG channels makes the task of emotion recognition very complex. To solve above problems,An EEG emotion recognition algorithm(SEE) based on three-stage feature selection strategy is proposed. EEG features were systematically extracted from time domain,frequency domain and spatial domain in this study. Based on the extracted EEG feature set,the features with no significant difference between classes were removed by t-test firstly,and then the recursive feature elimination strategy was used to select target related features. Finally,the final feature set was determined by the sequential backward feature selection strategy for the emotion recognition. The experimental results show that the model constructed in this study has better emotion recognition ability than other methods.Compared with the existing feature selection algorithms,SEE can filter out better feature subset with a low time complexity. In addition,emotion-associated EEG channels and frequency bands were detected. The experimental results show physiological significance of emotion,which may facilitate the development of emotion-targeted EEG devices.