Evaluating spatial normalization for SVM-based EEG decoding: A within- and between-subjects perspective
Open accessAbstract
Normalization is widely used in electroencephalogram (EEG)-based multivariate pattern classification (MVPC) to reduce magnitude differences across trials and subjects. However, the spatial normalization method as applied to EEG channel-based brain maps has been rarely investigated in EEG-based decoding tasks like event-related potential (ERP) experiments. Meanwhile, the effectiveness of spatial normalization across diverse experimental paradigms remains unclear. This study evaluated the impact of spatial normalization on decoding accuracy using the support vector machine (SVM). The analysis included nine experimental paradigms, with seven binary ERP paradigms, one four-class facial expression paradigm, and one sixteen-class orientation paradigm. Results showed that spatial normalization significantly improved the between-subjects decoding accuracy (Cohen’s d = 1 . 39 , p < 0 . 001 ) but did not enhance the within-subjects decoding accuracy. Additionally, the morphological fidelity of the difference wave was preserved after spatial normalization, as evidenced by the high similarity between the normalized and original ERP difference waves across the seven binary paradigms. We validated our findings across diverse experimental paradigms and demonstrated that spatial normalization effectively enhances between-subjects decoding accuracy using SVM while preserving the temporal consistency of ERP, offering a generalizable preprocessing approach for EEG-based cognitive, clinical, and brain–computer interface (BCI) applications. • The effect of spatial normalization was evaluated on the EEG decoding performance using SVM. • Nine diverse paradigms (seven binary, two multi-category) were examined in the between and within-subjects conditions. • Spatial normalization enhanced between-subjects decoding accuracy but not within-subjects decoding accuracy. • Spatially normalized data preserved the morphological fidelity of the difference waves as evidenced by seven binary paradigms.
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Cross-subject evaluation on ERP CORE has been reported using fold-based splits and time-point-wise LOSO decoding, but neither provides an epoch-level LOSO classification benchmark under deployment-compatible constraints such as causal filtering.
“Cross-subject evaluation on ERP CORE has been reported using fold-based splits [28] and time-point-wise LOSO decoding [29], but neither provides an epoch-level LOSO classification benchmark under deployment-compatible constraints such as causal filtering.”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [29]
To our knowledge, this is the first epoch-level LOSO benchmark on ERP CORE.
“To our knowledge, this is the first epoch-level LOSO benchmark on ERP CORE.”
From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [29]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371 · see citing works
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
Fisher, R. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 16,063 · see citing works
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992 · see citing works
Removing electroencephalographic artifacts by blind source separation
Jung, T., Makeig, S., Humphries, C., Lee, T.-W., McKeown, M. J., Iragui, V. J., & B, T. J. S. A. (2000) · Psychophysiology · doi:10.1111/1469-8986.3720163 · Cited by 3,342 · see citing works
Artificial neural networks: fundamentals, computing, design, and application
Basheer, I. A., & Hajmeer, M. N. (2000) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(00)00201-3 · Cited by 3,234 · see citing works
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., Haddadin, S., Liu, J., Cash, S. S., van der Smagt, P., et al. (2012) · Nature · doi:10.1038/nature11076 · Cited by 2,719 · see citing works
ERPLAB: an open-source toolbox for the analysis of event-related potentials
López‐Calderón, J., & Luck, S. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00213 · Cited by 2,599 · see citing works
Practical selection of SVM parameters and noise estimation for SVM regression
Cherkassky, V., & Ma, Y. (2003) · Neural Networks · doi:10.1016/s0893-6080(03)00169-2 · Cited by 2,034 · see citing works
Investigating the impact of data normalization on classification performance
Singh, D., & Singh, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105524 · Cited by 1,820 · see citing works
Jung, T., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & B, T. J. S. A. (2000) · Clinical Neurophysiology · doi:10.1016/s1388-2457(00)00386-2 · Cited by 1,326 · see citing works
EEG signal classification using PCA, ICA, LDA and support vector machines
Subaşı, A., & Gürsoy, M. İ. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.06.065 · Cited by 1,214 · see citing works
Ajiboye, A. B., Willett, F. R., Young, D. R., Memberg, W. D., Murphy, B. A., Miller, J. P., Walter, B. L., Sweet, J. A., Hoyen, H. A., Keith, M. W., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30601-3 · Cited by 862 · see citing works
Using EEG spectral components to assess algorithms for detecting fatigue
Jap, B. T., Lal, S., Fischer, P., & Bekiaris, E. D. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.12.043 · Cited by 737 · see citing works
Ang, K. K., Chua, K. S. G., Phua, K. S., Wang, C., Chin, Z. Y., Kuah, C. W. K., Low, W. C. J., & Guan, C. (2014) · Clinical EEG and Neuroscience · doi:10.1177/1550059414522229 · Cited by 542 · see citing works
Riemannian Approaches in Brain-Computer Interfaces: A Review
Yger, F., Berar, M., & Lotte, F. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2627016 · Cited by 420 · see citing works
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation
Aboalayon, K. A. I., Faezipour, M., Almuhammadi, W. S., & Moslehpour, S. (2016) · Entropy · doi:10.3390/e18090272 · Cited by 387 · see citing works
Epileptic seizure detection using hybrid machine learning methods
Subaşı, A., Kevrić, J., & Canbaz, M. A. (2017) · Neural Computing and Applications · doi:10.1007/s00521-017-3003-y · Cited by 309 · see citing works
Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System
Kiral-Kornek, I., Roy, S., Nurse, E. S., Mashford, B. S., Karoly, P. J., Carroll, T., Payne, D. E., Saha, S., Baldassano, S. N., O'Brien, T. J., et al. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.11.032 · Cited by 303 · see citing works
Bae, G., & Luck, S. J. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2860-17.2017 · Cited by 268 · see citing works
Huang, D., Qian, K., Fei, D.-Y., Jia, W., Chen, X., & Bai, O. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2190299 · Cited by 237 · see citing works
EEG-Based Estimation and Classification of Mental Fatigue
Trejo, L. J., Kubitz, K. A., Rosipal, R., Kochavi, R., & Montgomery, L. D. (2015) · Psychology · doi:10.4236/psych.2015.65055 · Cited by 236 · see citing works
ERP CORE: An open resource for human event-related potential research
Kappenman, E. S., Farrens, J. L., Zhang, W., Stewart, A. X., & Luck, S. J. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117465 · Cited by 223 · see citing works
Imagined speech can be decoded from low- and cross-frequency intracranial EEG features
Proix, T., Delgado Saa, J., Christen, A., Martin, S., Pasley, B. N., Knight, R. T., Tian, X., Poeppel, D., Doyle, W. K., Devinsky, O., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27725-3 · Cited by 212 · see citing works
Meltzer, J. A., Negishi, M., Mayes, L. C., & Constable, R. T. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.07.023 · Cited by 198 · see citing works
Quantification of the benefit from integrating MEG and EEG data in minimum ℓ2-norm estimation
Molins, A., Stufflebeam, S. M., Brown, E. N., & Hämäläinen, M. S. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.064 · Cited by 192 · see citing works
Rahman, M. A., Hossain, M. F., Hossain, M., & Ahmmed, R. (2019) · Egyptian Informatics Journal · doi:10.1016/j.eij.2019.10.002 · Cited by 181 · see citing works
EEG-based measurement system for monitoring student engagement in learning 4.0
Apicella, A., Arpaïa, P., Frosolone, M., Improta, G., Moccaldi, N., & Pollastro, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-09578-y · Cited by 177 · see citing works
Mazziotta, M., & Pareto, A. (2022) · Environmetrics · doi:10.1002/env.2730 · Cited by 152 · see citing works
Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system
Min, J., Wang, P., & Hu, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188756 · Cited by 146 · see citing works
Automatic sleep staging of EEG signals: recent development, challenges, and future directions
Phan, H., & Mikkelsen, K. B. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac6049 · Cited by 145 · see citing works
Watts, D., Pulice, R. F., Reilly, J. P., Brunoni, A. R., Kapczinski, F. P., & Passos, I. C. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02064-z · Cited by 136 · see citing works
Regional head tissue conductivity estimation for improved EEG analysis
Tucker, D. M., Eriksen, K. J., & Ferrée, T. C. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.887939 · Cited by 133 · see citing works
Loh, H. W., Ooi, C. P., Vicnesh, J., Oh, S. L., Faust, O., Gertych, A., & Acharya, U. R. (2020) · Applied Sciences · doi:10.3390/app10248963 · Cited by 129 · see citing works
EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention
Fan, J., Wade, J. W., Key, A. P. F., Warren, Z. E., & Sarkar, N. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2693157 · Cited by 121 · see citing works
Khodayari-Rostamabad, A., Hasey, G., MacCrimmon, D. J., Reilly, J. P., & de Bruin, H. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.05.009 · Cited by 95 · see citing works
Cross-Subject EEG-Based Emotion Recognition Through Neural Networks With Stratified Normalization
Fdez, J., Guttenberg, N., Witkowski, O., & Pasquali, A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.626277 · Cited by 86 · see citing works
Epileptic seizure detection using deep learning through min max scaler normalization
Deepa, B. G., & Ramesh, K. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns1.7801 · Cited by 80 · see citing works
On the effects of data normalization for domain adaptation on EEG data
Apicella, A., Isgrò, F., Pollastro, A., & Prevete, R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106205 · Cited by 79 · see citing works
Dynamically weighted ensemble classification for non-stationary EEG processing
Liyanage, S. R., Guan, C., Zhang, H., Ang, K. K., Xu, J., & Lee, T. H. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/036007 · Cited by 65 · see citing works
Personal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion Recognition
Chen, H., Sun, S., Li, J., Yu, R., Li, N., Li, X., & Hu, B. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3137857 · Cited by 63 · see citing works
Towards User-Friendly Spelling with an Auditory Brain-Computer Interface: The CharStreamer Paradigm
Höhne, J., & Tangermann, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098322 · Cited by 55 · see citing works
Evaluation of L1 and L2 minimum norm performances on EEG localizations
Silva, C., Maltez, J., Trindade, E. A., Arriaga, A., & Ducla‐Soares, E. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.02.009 · Cited by 43 · see citing works
Arevalillo-Herráez, M., Cobos, M., Roger, S., & García-Pineda, M. (2019) · Italian National Conference on Sensors · doi:10.3390/s19132999 · Cited by 42 · see citing works
Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data
Zhang, G., Carrasco, C. D., Winsler, K., Bahle, B., Cong, F., & Luck, S. J. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120625 · Cited by 40 · see citing works
Self-citationDecoding the temporal dynamics of affective scene processing
Bo, K., Cui, L., Yin, S., Hu, Z., Hong, X., Kim, S., Keil, A., & Ding, M. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119532 · Cited by 38 · see citing works
Decoding semantic relatedness and prediction from EEG: A classification method comparison
Trammel, T., Khodayari, N., Luck, S. J., Traxler, M. J., & Swaab, T. Y. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120268 · Cited by 35 · see citing works
Sources of bias in single‐trial normalization procedures
Ciuparu, A., & Muresan, R. C. (2016) · European Journal of Neuroscience · doi:10.1111/ejn.13179 · Cited by 34 · see citing works
Noah, S., Powell, T., Khodayari, N., Olivan, D., Ding, M., & Mangun, G. R. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2685-19.2020 · Cited by 34 · see citing works
Using multivariate pattern analysis to increase effect sizes for event‐related potential analyses
Carrasco, C. D., Bahle, B., Simmons, A. M., & Luck, S. J. (2024) · Psychophysiology · doi:10.1111/psyp.14570 · Cited by 34 · see citing works
Decoding the temporal dynamics of spoken word and nonword processing from EEG
McMurray, B., Sarrett, M. E., Chiu, S. L., Black, A. K., Wang, A., Canale, R. R., & Aslin, R. Ν. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119457 · Cited by 18 · see citing works
Decoding Part-of-Speech from Human EEG Signals
Murphy, A., Bohnet, B., McDonald, R., & Noppeney, U. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.156 · Cited by 17 · see citing works
Deen, O. M. T. A., Fan, S., & Shieh, J.-S. (2025) · Sensors · doi:10.3390/s25041150 · Cited by 14 · see citing works
Zhang, G., & Luck, S. J. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121304 · Cited by 14 · see citing works
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ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification
Genevier Wyman, C., & Hirshfield, L. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae9f95
“Cross-subject evaluation on ERP CORE has been reported using fold-based splits [28] and time-point-wise LOSO decoding [ 29], but neither provides an epoch-level LOSO classification benchmark under deployment-compatible constraints such as causal filtering.”
Context Matters: Investigating Physiological Embeddings for Stress Detection Generalization
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Zeng, Y., Li, Z., Wan, M., Xiao, Y., Chen, X., Qin, Y., & Yan, B. (2026) · International Journal of Neural Systems · doi:10.1142/s0129065727500328
Self-citation
- Authors
- Qin, Y., Xu, Q., Kujala, T., Wang, X., & Cong, F.
- Affiliations
- Dalian University of Technology (CN)
- University of Jyväskylä (FI)
- Corresponding author
- Wang, Xiaoshuang
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2026-05
- Volume
- 116
- Pages
- 109535
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W7119600739
- Cited by
- 1
- References
- 54
- Citations by year
- 2026: 1
- FWCI
- 4.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biomedical Signal Processing and Control (publishedVersion, cc-by)
- Jyväskylä University Digital Archive (University of Jyväskylä) (submittedVersion, cc-by)
- Funders
- China Scholarship Council (201906060242)
- National Natural Science Foundation of China (91748105, 82402431)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- spatial normalization, EEG decoding, support vector machine, event-related potentials, multivariate pattern classification, difference waves, facial expression recognition, orientation discrimination, brain-computer interface, EEG preprocessing
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