Effects of Data Augmentation Method Borderline-SMOTE on Emotion Recognition of EEG Signals Based on Convolutional Neural Network

Abstract

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially electroencephalogram signals, has become a popular research topic and attracted wide attention. However, the imbalance of the data sets themselves, affective features' extraction from electroencephalogram signals, and the design of classifiers with excellent performance, pose a great challenge to the subject. Motivated by the outstanding performance of deep learning approaches in pattern recognition tasks, we propose a method based on convolutional neural network with data augmentation method Borderline-synthetic minority oversampling technique. First, we obtain 32-channel electroencephalogram signals from DEAP data set, which is the standard data set of emotion recognition. Then, after data pre-processing, we extract features in frequency domain and data augmentation based on the data augmentation algorithm above for getting more balanced data. Finally, we train a one dimensional convolutional neural network for three classification on two emotional dimensions valence and arousal. Meanwhile, the proposed method is compared with some traditional machine learning methods and some existing methods by other researchers, which is proved to be effective in emotion recognition, and the average accuracy rate of 32 subjects on valence and arousal are 97.47% and 97.76% respectively. Compared with other existing methods, the performance of the proposed method with data augmentation algorithm Borderline-SMOTE shows its advantage in affective emotional recognition than that without Borderline-SMOTE.

Emotion Recognition by Correlating Facial Expressions and EEG Analysis

“…However, one of the main challenges is that these patterns are sought in large time windows (the time the evoked potential lasts, which can range from a few seconds to minutes), which implies that many other events can affect the experimental process, such as eye movements, facial muscles or cognitive states unrelated to the experiment. The analysis through ML has been proven to diagnose medical and cognitive phenomena conditions successfully [5,[22][23][24][25][26][27].…”

ISTRF: Identification of sucrose transporter using random forest

“…It uses the KNN algorithm to calculate the k nearest neighbors of each minority class sample, randomly selects N samples, and performs random linear interpolation on the k nearest neighbors to construct new minority class samples. However, it does not consider the position of the adjacent majority class samples, which usually leads to the phenomenon of sample overlap and affects the classification effect ( Chen et al, 2021 ). Borderline-SMOTE ( Han et al, 2005 ) is an improved oversampling algorithm based on SMOTE.…”

Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification

“…For the DEAP dataset, the 1D-CNN model was utilized for classifications of two emotional dimensions: valence and arousal. B-SMOTE was employed to acquire a more homogeneous set of features of EEG signals [ 34 ]. For classifying HSIs, rotation forest has been combined with dynamic SMOTE [ 35 ], where SMOTE is applied to the imbalanced classes before each rotation tree is constructed.…”