Data augmentation effects using borderline-SMOTE on classification of a P300-based BCI
Abstract
In this study, we addressed a problem of imbalance in the size of event-related potentials (ERPs) between target and nontarget stimulation events, which is intrinsic to the odd-ball paradigm used in P300-based brain-computer interfaces (BCIs). Specifically, we investigated whether data augmentation could remedy this problem and improve BCI performance. We investigated a data augmentation technique, borderline-Synthetic Minority Over-sampling Technique (SMOTE). We focused on the effects of data augmentation on users with poor BCI performance. The EEG data were obtained from experiments with the P300-based BCI system developed for controlling 3 home appliances (Lamp, Door lock, Bluetooth speaker), where the classifier was designed by a support vector machine (SVM) and a convolutional neural network (CNN). As a result, although Borderline-SMOTE did not significantly change the overall BCI performance, it significantly improved the performance of poor performers. This suggests that data augmentation can offer an effective way to increase the performance of users illiterate to P300-based BCIs.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Borderline-SMOTE enhanced the robustness of decision boundaries to improve classification accuracy of P300 based on BCIs.
“Results show that the proposed methods could enhance the robustness of decision boundaries to improve the classification accuracy of P300 based on BCIs.”
From Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review (2021) · cites this paper as Lee et al., 2020
The citation lists haven’t been fetched yet
References 16 · Cited by 32
- Authors
- Lee, T., Kim, M., & Kim, S.-P.
- Affiliations
- Ulsan National Institute of Science and Technology (KR)
- Venue
- 2020 8th International Winter Conference on Brain-Computer Interface (BCI)
- Publisher
- IEEE
- Published
- 2020-02
- Pages
- 1-4
- OpenAlex
- W3015264729
- Cited by
- 32
- References
- 16
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 3 · 2023: 7 · 2022: 7 · 2021: 6
- FWCI
- 6.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Winter Conference on Brain-Computer Interface (BCI) (publishedVersion)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Keywords
- P300, brain-computer interface, data augmentation, Borderline-SMOTE, class imbalance, event-related potentials, oddball paradigm, EEG classification, support vector machine, convolutional neural networks, home appliance control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
