A feature extraction method combining differential entropy with Linear Discriminant Analysis improved EEG classification average accuracy by 68% compared to the original dataset.
Combining differential entropy with LDA significantly improves the accuracy and reduces the time complexity of EEG-based emotion classification.
Effect estimate: 68% increase in average accuracy
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 Discriminant Analysis (LDA) that can be applied in feature extraction of emotional EEG signals. We use a three-category sentiment EEG dataset to conduct experiments. The experimental results show that the proposed feature extraction method can significantly improve the performance of the EEG classification: Compared with the result of the original dataset, the average accuracy increases by 68%, which is 7% higher than the result obtained when only using differential entropy in feature extraction. The total execution time shows that the proposed method has a lower time complexity.
Chen et al. (Fri,) conducted a other in Emotion recognition from EEG signals. Feature extraction method combining differential entropy with Linear Discriminant Analysis (LDA) vs. Original dataset and differential entropy alone was evaluated on EEG classification average accuracy (68% increase in average accuracy). A feature extraction method combining differential entropy with Linear Discriminant Analysis improved EEG classification average accuracy by 68% compared to the original dataset.