Sadegh-Zadeh Seyed-Ali, Sadeghzadeh Nasrin, Soleimani Ommolbanin, Shiry Ghidary Saeed, Movahedi Sobhan, Mousavi Seyed-Yaser
Department of Computing, University of Staffordshire Stoke-on-Trent, United Kingdom.
Faculty of Mathematics, University of Qom Qom, Iran.
Am J Neurodegener Dis. 2024 Oct 25;13(4):23-33. doi: 10.62347/ZWRY8401. eCollection 2024.
The aim of this study is to evaluate the impact of various dimensionality reduction methods, including principal component analysis (PCA), Laplacian score, and Chi-square feature selection, on the classification performance of an electroencephalogram (EEG) dataset.
We applied dimensionality reduction techniques, including PCA, Laplacian score, and Chi-square feature selection, and assessed their impact on the classification performance of EEG data using linear regression, K-nearest neighbour (KNN), and Naive Bayes classifiers. The models were evaluated in terms of their classification accuracy and computational efficiency.
Our findings suggest that all dimensionality reduction strategies generally improved or maintained classification accuracy while reducing the computational load. Notably, PCA and Autofeat techniques led to increased accuracy for the models.
The use of dimensionality reduction techniques can enhance EEG data classification by reducing computational demands without compromising accuracy. These results demonstrate the potential for these techniques to be applied in scenarios where both computational efficiency and high accuracy are desired. The code used in this study is available at https://github.com/movahedso/Emotion-analysis.
本研究旨在评估包括主成分分析（PCA）、拉普拉斯分数和卡方特征选择在内的各种降维方法对脑电图（EEG）数据集分类性能的影响。
我们应用了包括PCA、拉普拉斯分数和卡方特征选择在内的降维技术，并使用线性回归、K近邻（KNN）和朴素贝叶斯分类器评估它们对EEG数据分类性能的影响。根据分类准确率和计算效率对模型进行评估。
我们的研究结果表明，所有降维策略在降低计算量的同时，通常都提高或保持了分类准确率。值得注意的是，PCA和自动特征提取技术提高了模型的准确率。
使用降维技术可以通过降低计算需求来增强EEG数据分类，而不会影响准确率。这些结果证明了这些技术在需要计算效率和高精度的场景中应用的潜力。本研究中使用的代码可在https://github.com/movahedso/Emotion-analysis获取。
