Spatiotemporal Representations of Rapid Visual Target Detection: A Single-Trial EEG Classification Algorithm
Abstract
Brain computer interface applications, developed for both healthy and clinical populations, critically depend on decoding brain activity in single trials. The goal of the present study was to detect distinctive spatiotemporal brain patterns within a set of event related responses. We introduce a novel classification algorithm, the spatially weighted FLD-PCA (SWFP), which is based on a two-step linear classification of event-related responses, using fisher linear discriminant (FLD) classifier and principal component analysis (PCA) for dimensionality reduction. As a benchmark algorithm, we consider the hierarchical discriminant component Analysis (HDCA), introduced by Parra, et al. 2007. We also consider a modified version of the HDCA, namely the hierarchical discriminant principal component analysis algorithm (HDPCA). We compare single-trial classification accuracies of all the three algorithms, each applied to detect target images within a rapid serial visual presentation (RSVP, 10 Hz) of images from five different object categories, based on single-trial brain responses. We find a systematic superiority of our classification algorithm in the tested paradigm. Additionally, HDPCA significantly increases classification accuracies compared to the HDCA. Finally, we show that presenting several repetitions of the same image exemplars improve accuracy, and thus may be important in cases where high accuracy is crucial.
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LDA cannot handle various components of ERPs.
“However, such a method cannot handle various components of ERPs [42], [49], [50].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [42]
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2014 Index IEEE Transactions on Biomedical Engineering Vol. 61
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- Authors
- Fuhrmann Alpert, G., Manor, R., Spanier, A. B., Deouell, L. Y., & Geva, A. B.
- Affiliations
- Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel
- Ben-Gurion University of the Negev (IL)
- Hebrew University of Jerusalem (IL)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2014-08
- Volume
- 61
- Issue
- 8
- Pages
- 2290-2303
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2042441570
- Cited by
- 74
- References
- 29
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 12 · 2023: 6 · 2022: 8 · 2021: 10 · 2020: 6 · 2019: 5 · 2018: 6 · 2017: 5 · 2016: 5 · 2015: 1 · 2014: 1
- FWCI
- 1.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Israeli Defense Ministry
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- single-trial EEG classification, rapid serial visual presentation, visual target detection, event-related potentials, linear discriminant analysis, dimensionality reduction, brain-computer interface, object category recognition
- MeSH terms
- Adult, Algorithms, Electroencephalography, Evoked Potentials, Visual, Female, Humans, Male, Pattern Recognition, Automated, Photic Stimulation, Principal Component Analysis, Young Adult, Wavelet Analysis, Brain-Computer Interfaces
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 ›
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