Classification of imperfectly time-locked image RSVP events with EEG device.
Open accessAbstract
Classification based on EEG data in an RSVP experiment is considered. Although the latency in neural response relative to the stimulus onset time may be more realistically considered to vary across trials due to factors such as subject fatigue and environmental distractions, it is nevertheless assumed to be time-locked to the stimulus in most of the existing work as a means to alleviate the computational complexity. We consider here a more practical scenario that allows variation in response latency and develop a rigorous statistical formulation for modeling the uncertainty within the varying latency coupled with a likelihood ratio test (LRT) for classification. The new model not only improves the EEG classification performance, but also may predict the true stimulus onset time when this information is not precisely available. We test the proposed LRT algorithm on an EEG data set from an image RSVP experiment and show that, by admitting the latency variation, the proposed approach consistently outperforms a method that relies on perfect time-locking (AUC: 0.88 vs 0.86), especially when the stimulus onset time is not precisely available (AUC: 0.84 vs 0.71). Furthermore, the predicted stimulus onset times are highly enriched around the true onset time with p-value = 5.2 x 10(44).
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EEG based on VEP paradigms prefer to employ the time-series signal as input, which has the strict requirement of being time-locked and contains more obvious features in time sequence.
“While the EEG based on VEP paradigms prefer to employ the time-series signal as the input, which has the strict requirement of being time-locked and contains more obvious features in time sequence (Basar et al., 1995; Kolev and Schurmann, 2009; Meng et al., 2014).”
From Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review (2021) · cites this paper as Meng et al., 2014
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Self-citation
- Authors
- Meng, J., Meriño, L. M., Robbins, K., & Huang, Y.
- Affiliations
- Broad Institute (US)
- The University of Texas at San Antonio (US)
- The University of Texas at San Antonio Health Science Center (US)
- Corresponding author
- Huang, Yufei
- Venue
- Neuroinformatics
- Published
- 2013-09-15
- Volume
- 12
- Issue
- 2
- Pages
- 261-275
- ISSN
- 1539-2791 (ISSN-L)
- 1559-0089
- OpenAlex
- W2050591368
- Cited by
- 4
- References
- 21
- Citations by year
- 2024: 1 · 2021: 1 · 2019: 1 · 2015: 1
- FWCI
- 0.18
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc
- www.springer.com/tdm (tdm)
- Locations
- Neuroinformatics (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Springer US
- Funders
- National Institutes of Health
- National Institute on Minority Health and Health Disparities (G12MD007591)
- Army Research Laboratory
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- EEG classification, rapid serial visual presentation, likelihood ratio test
- MeSH terms
- Brain, Electroencephalography, Evoked Potentials, Visual, Humans, Visual Perception, Likelihood Functions
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