A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition.

ABSTRACT: Feature extraction of electroencephalography (EEG) signals plays a significant role in the wearable computing field. Due to the practical applications of EEG emotion calculation, researchers often use edge calculation to reduce data transmission times, however, as EEG involves a large amount of data, determining how to effectively extract features and reduce the amount of calculation is still the focus of abundant research. Researchers have proposed many EEG feature extraction methods. However, these methods have problems such as high time complexity and insufficient precision. The main purpose of this paper is to introduce an innovative method for obtaining reliable distinguishing features from EEG signals. This feature extraction method combines differential entropy with Linear Discrimin

SUBMITTER: Chen DW 

PROVIDER: S-EPMC6479375 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature