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 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.
Highlights
Electroencephalography (EEG) is a means of data acquisition via sensors [1,2]
This paper proposes a feature extraction method based on the fusion of differential entropy and Linear Discriminant Analysis (LDA) (Figure 1, yellow area)
Existing research has proved that the EEG signal is roughly consistent with a Gaussian distribution through band-pass filtering and signal processing, which lays a solid foundation for the feature extraction of EEG signals, such as signal feature extraction methods based on Fourier transformation, signal feature extraction methods based on differential entropy have been proposed
Summary
Electroencephalography (EEG) is a means of data acquisition via sensors [1,2]. In actual applications, due to the large amount of data involved in EEG, the collected data is often used in the mobile edge computing server for moving edge calculation. The Brain–Computer Interface (BCI), known as the direct neural interface, is an interdisciplinary cutting-edge technology. It is a direct connection pathway established between a human or animal brain (or culture of brain cells) and an external device [3,4,5,6,7]. The role of BCI is to establish communication between the human brain and external computers or other intelligent electronic devices [8,9]. Emotional recognition is a very important part of BCI. Emotional recognition generally refers to the acquisition of an individual’s physiological or non-physiological signals to automatically identify the individual’s
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