ArticleAmerican journal of neurodegenerative disease2024
Comparative analysis of dimensionality reduction techniques for EEG-based emotional state classification.
Article in American journal of neurodegenerative disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Eye Movement Patterns as Robust Biomarkers for Schizophrenia Identification Using a Novel Data Transformation Approach.Journal of eye movement research · 2026Article
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- A generalized three-tier hybrid model for classifying unseen (IoT devices) in smart home environments.Scientific reports · 2025Article
- Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.BioMedInformatics · 2025Article
- Curvature estimation techniques for advancing neurodegenerative disease analysis: a systematic review of machine learning and deep learning approaches.American journal of neurodegenerative disease · 2025Review
- Neural reshaping: the plasticity of human brain and artificial intelligence in the learning process.American journal of neurodegenerative disease · 2024Review
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Authors and funding
6 authors.
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Abstract
objectivesThe 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.
methodsWe 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.
resultsOur 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.
conclusionsThe 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.
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