The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion Classification Article Swipe
Zubaidah Al-Mashhadani
,
Nasrin Bayat
,
Ibrahim F. Kadhim
,
Renoa Choudhury
,
Joon‐Hyuk Park
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13148274
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13148274
Electroencephalography (EEG) signals have diverse applications in brain-computer interfaces (BCIs), neurological condition diagnoses, and emotion recognition across healthcare, education, and entertainment domains. This paper presents a robust method that leverages Riemannian geometry to enhance the accuracy of EEG-based emotion classification. The proposed approach involves adaptive feature extraction using principal component analysis (PCA) in the Euclidean space to capture relevant signal characteristics and improve classification performance. Covariance matrices are derived from the extracted features and projected onto the Riemannian manifold. Emotion classification is performed using the minimum distance to Riemannian mean (MDRM) classifier. The effectiveness of the method was evaluated through experiments on four datasets, DEAP, DREAMER, MAHNOB, and SEED, demonstrating its generalizability and consistent accuracy improvement across different scenarios. The classification accuracy and robustness were compared with several state-of-the-art classification methods, which supports the validity and efficacy of using Riemannian geometry for enhancing the accuracy of EEG-based emotion classification.
Related Topics
Concepts
Generalizability theory
Riemannian geometry
Electroencephalography
Artificial intelligence
Pattern recognition (psychology)
Computer science
Robustness (evolution)
Emotion classification
Classifier (UML)
Principal component analysis
Feature extraction
Mathematics
Psychology
Statistics
Geometry
Biochemistry
Psychiatry
Gene
Chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13148274
- https://www.mdpi.com/2076-3417/13/14/8274/pdf?version=1689597575
- OA Status
- gold
- Cited By
- 4
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384666052
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4384666052Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app13148274Digital Object Identifier
- Title
-
The Efficacy and Utility of Lower-Dimensional Riemannian Geometry for EEG-Based Emotion ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-17Full publication date if available
- Authors
-
Zubaidah Al-Mashhadani, Nasrin Bayat, Ibrahim F. Kadhim, Renoa Choudhury, Joon‐Hyuk ParkList of authors in order
- Landing page
-
https://doi.org/10.3390/app13148274Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/13/14/8274/pdf?version=1689597575Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/13/14/8274/pdf?version=1689597575Direct OA link when available
- Concepts
-
Generalizability theory, Riemannian geometry, Electroencephalography, Artificial intelligence, Pattern recognition (psychology), Computer science, Robustness (evolution), Emotion classification, Classifier (UML), Principal component analysis, Feature extraction, Mathematics, Psychology, Statistics, Geometry, Biochemistry, Psychiatry, Gene, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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