Abstract:

With the development of brain-computer interface technology, emotion recognition based on Electroencephalogram(EEG) signals has become a research hotspot. This paper compares the performance of different kernel functions in Support Vector Machine(SVM) for EEG-based emotion recognition tasks, and contrasts them with common machine learning methods such as decision trees, random forests, and neural networks. Based on the DEAP dataset, by analyzing the performance of various kernel functions (linear, radial basis function, and polynomial) and other models, this paper found that random forest achieves the best performance in terms of accuracy and AUC values. Linear kernel SVM is suitable for linearly separable data, while radial basis function and polynomial kernels show relatively poorer performance. Additionally, this paper explores the performance of neural networks and proposes future research directions for optimizing models and kernel function selection. It aims to provide valuable insights into EEG-based emotion recognition and advance the development of BCI technology.

Key words: electroencephalogram, emotion recognition, support vector machine, kernel function, machine learning