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Single-Trial Classification of Event-Related Potentials in Rapid Serial Visual Presentation Tasks Using Supervised Spatial Filtering
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Accurate detection of single-trial event-related potentials (ERPs) in the electroencephalogram (EEG) is a difficult problem that requires efficient signal processing and machine learning techniques. Supervised spatial filtering methods that enhance the discriminative information in EEG data are commonly used to improve single-trial ERP detection. We propose a convolutional neural network (CNN) with a layer dedicated to spatial filtering for the detection of ERPs and with training based on the maximization of the area under the receiver operating characteristic curve (AUC). The CNN is compared with three common classifiers: 1) Bayesian linear discriminant analysis; 2) multilayer perceptron (MLP); and 3) support vector machines. Prior to classification, the data were spatially filtered with xDAWN (for the maximization of the signal-to-signal-plus-noise ratio), common spatial pattern, or not spatially filtered. The 12 analytical techniques were tested on EEG data recorded in three rapid serial visual presentation experiments that required the observer to discriminate rare target stimuli from frequent nontarget stimuli. Classification performance discriminating targets from nontargets depended on both the spatial filtering method and the classifier. In addition, the nonlinear classifier MLP outperformed the linear methods. Finally, training based AUC maximization provided better performance than training based on the minimization of the mean square error. The results support the conclusion that the choice of the systems architecture is critical and both spatial filtering and classification must be considered together.
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“Research has extensively explored the combination of xDAWN with classification algorithms, yielding promising results.”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [29]
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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,123
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Updating P300: An integrative theory of P3a and P3b
Polich, J. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2007.04.019 · Cited by 7,919
(2005) · Cited by 5,977
Mackay, D. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.415 · Cited by 4,440
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228
Research works printed but not linked: 2
- golub, 1996, Matrix Computations
- sharbrough, 1990, Guidelines for Standard Electrode Position Nomenclature
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Carabez, E., Sugi, M., Nambu, I., & Wada, Y. (2017) · Applied Sciences · doi:10.3390/app7111197 · Cited by 9
Lin, Z., Zeng, Y., Tong, L., Zhang, H., Zhang, C., & Yan, B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184713 · Cited by 9
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Raza, H., Prasad, G., Li, Y., & Cecotti, H. (2014) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2014.6999160 · Cited by 9
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Nayak, P., Meenakshi, S., & Medikondu, N. R. (2023) · Research on Biomedical Engineering · doi:10.1007/s42600-023-00321-8 · Cited by 3
Cui, Y., Xie, S., Xie, X., Duan, X., & Gao, C. (2022) · PubMed Central · doi:10.7507/1001-5515.202104049 · Cited by 3
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Self-citationCecotti, H., Eckstein, M. P., & Giesbrecht, B. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943832 · Cited by 3
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Jindal, M., Bajal, E., & Kazim, A. (2023) · Wiley eBooks · doi:10.1002/9781119857655.ch2 · Cited by 3
Improving single-trial detection of event-related potentials through artificial deformed signals
Cecotti, H., & Rivet, B. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944529 · Cited by 2
Self-citationFrequency filter networks for EEG-based recognition
Yanagimoto, M., Sugimoto, C., & Nagao, T. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122614 · Cited by 2
Single-trial detection of realistic images with magnetoencephalography
Cecotti, H., & Prasad, G. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280778 · Cited by 1
Self-citationKao, C. (2017) · International Journal of Computers and Applications · doi:10.1080/1206212x.2017.1313490 · Cited by 1
Electroencephalographic (EEG) based Deep Learning (DL): A Comparative Review
Mohammed, R. S., & Al-Hamadani, A. A. (2023) · Al-Iraqia Journal of Scientific Engineering Research · doi:10.58564/ijser.2.1.2023.59 · Cited by 1
Application of Convolutional Neural Network Method in Brain Computer Interface
Chen, L., Deng, W., & Ji, C. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2078/1/012044 · Cited by 1
Metadata
- Authors
- Cecotti, H., Eckstein, M. P., & Giesbrecht, B.
- Affiliations
- University of Ulster (GB)
- University of California, Santa Barbara (US)
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2014-11
- Volume
- 25
- Issue
- 11
- Pages
- 2030-2042
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2044310907
- Cited by
- 109
- References
- 63
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 9 · 2023: 8 · 2022: 9 · 2021: 5 · 2020: 13 · 2019: 17 · 2018: 10 · 2017: 15 · 2016: 6 · 2015: 9 · 2014: 3
- FWCI
- 6.13
- Type
- journal article
- Language
- en
- Open access
- green
- pure.ulster.ac.uk
- Locations
- IEEE Transactions on Neural Networks and Learning Systems (publishedVersion)
- PubMed (publishedVersion)
- Ulster University Research Portal (Ulster University) (acceptedVersion, other-oa)
- Funders
- Institute for Collaborative Biotechnologies through the U.S. Army Research Office
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- single-trial event-related potentials, rapid serial visual presentation, convolutional neural networks, AUC maximization, common spatial pattern, EEG classification, target detection, nonlinear classifiers, multilayer perceptron, support vector machine
- MeSH terms
- Adolescent, Adult, Algorithms, Brain Mapping, Electroencephalography, Evoked Potentials, Female, Filtration, Humans, Male, Photic Stimulation, Reaction Time, ROC Curve, Signal Processing, Computer-Assisted, Visual Perception, Neural Networks, Computer, Area Under Curve, Young Adult, Support Vector Machine
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