Dataset for article entitled "An empirical evaluation of methodologies used for emotion recognition via EEG signals"

The data is split into two parts according to the two experiments described within the article. The dataset includes movies and python codes for classifying emotions from experiment 1, and EEG and ERP measurements from experiment 2 along with associated code for analyzing those data.

Experiment 1 tests the validity of the SEED dataset collated by Zheng, Dong, & Lu (2014) and, subsequently, our own stimuli. The objective was to test whether previous literature using such datasets as the aformentioned dataset by Zheng et al. is purportedly classifying between emotions based on emotion-related signals of interest and/or non-emotional ‘noise’.

Experiment 2 used stimuli that have been well-validated within the psychological literature as reliably evoking specific embodiments of emotions within the viewer, namely the NimStim face and ADFES-BIV datasets with the objective of classifying a person's emotional status using EEG.

All data was processed and analyses run in MATLAB or Python. All datasets used are included within the folders accompanied by the MATLAB or Python scripts for collating separable matrices and running the action.

根据文章中描述的两种实验，数据被分为两个部分。该数据集包括来自实验1的用于情感分类的电影和Python代码，以及来自实验2的EEG和ERP测量数据及其相应的数据分析代码。

实验1旨在验证Zheng、Dong和Lu（2014）所编纂的SEED数据集的有效性，以及后续我们所使用的刺激物。其目标在于检验先前文献中，诸如Zheng等人所提数据集等的使用是否确实基于感兴趣的与情感相关的信号以及/或非情感性的‘噪声’对情感进行分类。

实验2采用了在心理学文献中得到充分验证的刺激物，这些刺激物能够可靠地唤起观众的具体情感体验，具体包括NimStim面部表情和ADFES-BIV数据集，旨在利用EEG对个人的情绪状态进行分类。

所有数据均使用MATLAB或Python进行处理和分析。所有使用的数据集均包含在相应的文件夹中，并附有MATLAB或Python脚本，用于收集可分离的矩阵和运行相关操作。