Combined Weighted Feature Correlation Approach for Enhanced EEG-Based Emotion Recognition Across Diverse Datasets

摘要

The paper is centered on EEG-based emotion recognition, which is an emerging domain in neuroscience and emotional computing. The traditional linear methods are insufficient as it is impossible to represent complicated emotional structure in video. To address this, the authors propose a new model, Combined Weighted Feature Correlation (CWFC), that capture the emotional relationships. After band pass filtering, Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) are employed by the model to extract non-linear features from EEG. In this case best feature is selected by LSTM and these features are evaluated by Random Forest Classifier. Introduction Generative Adversarial Network (GAN) based data augmentation gives 88% (for valence) and 86% (for Evaluation of Test Results The proposed GAN based arousal) accuracy on the DEAP dataset which is very high in data augmentation has been able to improve the performance when performance. The model validates that CWFC is beneficial across datasets, as it delivers an overall classification accuracy of 89% for emotional states on the SEED dataset.

摘要

Emotion recognition using EEG Signals is an active area of research in affective computing and neuroscience, aiding scientists into their quest to understand the brain processes involved in emotions. In contrast to the existing works which focused on linear EEG features, the present work concentrates on non-linear features in order to better characterize the finer changes of emotional responding. Inspired by this, in current paper, proposed method is called Combined Weighted Feature Correlation (CWFC) to investigate the effect of non-linear components on the performance of emotion detection. As a first step, the EEG data is collected and pre-processed using bandpass filters to separate the frequency bands such as beta, alpha, gamma, delta, and theta. To detect intricate emotional patterns, features extraction is performed by Independent Component Analysis (ICA), DWT (Discrete Wavelet Transform) and FFT (Fast Fourier Transform). These features can be built as combined features into the CWFCC model to improve its power as well as its overall predictive accuracy. Then apply a Random Forest classifier to see how significant are these combined features. Proposed work integrates GAN data augmentation after optimal feature selection (with an emphasis on LSTM). On the DEAP dataset, this augmentation results in astonishing 88% valence accuracy and 86% arousal accuracy, which improves the recognition accuracy of emotion remarkably. Proposed model achieved valence and arousal accuracy of 62% and 65%, respectively, on the early DEAP without GAN data augmentation. Moreover, this model can distinguish between SEED dataset during Neutral emotional states, Negative, and Positive with an average accuracy of 89%. The proposed paper proclaims the superior performance of CWFC model with GAN based data augmentation on EEG for emotion recognition over different datasets through in-depth analysis and comparative study.

引言

Emotion recognition is important in human interaction and human communication. In order to establish successful interpersonal connections, one must be able to perceive and understand emotions, thus having far-reaching implications in domains as diverse as psychology, neurology, and human-computer interaction¹. In past few years, the electroencephalogram (EEG) based BCI (Brain Computer Interface) has been a merit tool that researchers have focused on to detect emotions. Although EEG-based BCI provide a direct window into the electrical activity of the human brain and an appropriate research tool for exploring emotional states. The automatic and ....

方案

The research did not include any direct engagement with human subjects. All datasets utilized in this study were sourced from publicly accessible platforms and were used in accordance with their stated usage policies. As such, formal ethical approval was not necessary.

1. Computational Environment Preparation

1. Prepare a Python environment (version 3.8 or higher) on a standard workstation.
2. Install TensorFlow (v2.x), Keras, Scikit-learn, NumPy, SciPy, MNE, and PyWavelets using pip.
3. Fix the random seed (seed = 42) in NumPy and TensorFlow to ensure consistent experimental results.

结果

The collection of two different EEG datasets; DEAP and SEED formed the first stage of the analysis. Figure 5 shows the graph of Valence-Arousal of LAHV, HALV, LALV, and HAHV are short form of low arousal high valence, high arousal low valence, low arousal low valence and high arousal high valence respectively. Four categories of psychological response have been reported: HAHV, LAHV, HALV, and LALV. Two fundamental emotional features, valence and arousal, are associated with each state and are measured by their mean and s.......

讨论

In this paper, research conducted a thorough investigation of the EEG-based emotion recognition on two benchmark datasets (i.e., DEAP and SEED) and showed that the proposed CWFC model can effectively capture emotion-related discriminative information. Unlike previous works based essentially on linear descriptions or feature types, the proposed methodology unifies various nonlinear feature extractions that are interconnected and leverages them via a weighted correlation model to infer functional relationships. This multi-.......

材料

本文使用的材料清单

姓名	公司	目录编号	评论
DEAP 数据集	伦敦玛丽女王大学	v1.0	脑电图情绪数据集
DREAMER 数据集	IEEE 数据端口	公开发布	脑电图情绪数据集
克拉斯	谷歌有限责任公司	2.x（TensorFlow 后端）	神经网络建模
工作站	联想	Intel Core i7,16GB 内存，8GB NVIDIA 显卡	模型训练与计算
MNE-Python	跨国开发商	1.x	脑电预处理
数字派	NumPy 开发者	1.x	数值计算
操作系统	Microsoft公司	Windows 11（64位）	计算平台
蟒蛇	Python 软件基础	3.8+	编程环境
Py小波	PyWavelets 开发者	1.x	小波分解
Scikit-learn	scikit-learn 开发者	1.x	机器学习工具
科幻	SciPy 社区	1.x	信号处理与重采样
SEED 数据集	上海交通大学BCMI实验室	公开发布	脑电图情绪数据集
张量流	谷歌有限责任公司	2.x	深度学习框架