ArticleGigaScience2014
Event-related potential datasets based on a three-stimulus paradigm.
Article in GigaScience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Brain-Computer Interface: Advancement and Challenges.Sensors (Basel, Switzerland) · 2021Review
- EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy.GigaScience · 2019Article
- Event-related potential data from a guess the number brain-computer interface experiment on school children.Scientific data · 2017Article
- Stacked Autoencoders for the P300 Component Detection.Frontiers in neuroscience · 2017Article
- Event-related potential datasets based on a three-stimulus paradigm.GigaScience · 2014Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe event-related potentials technique is widely used in cognitive neuroscience research. The P300 waveform has been explored in many research articles because of its wide applications, such as lie detection or brain-computer interfaces (BCI). However, very few datasets are publicly available. Therefore, most researchers use only their private datasets for their analysis. This leads to minimally comparable results, particularly in brain-computer research interfaces. Here we present electroencephalography/event-related potentials (EEG/ERP) data. The data were obtained from 20 healthy subjects and was acquired using an odd-ball hardware stimulator. The visual stimulation was based on a three-stimulus paradigm and included target, non-target and distracter stimuli. The data and collected metadata are shared in the EEG/ERP Portal.
findingsThe paper also describes the process and validation results of the presented data. The data were validated using two different methods. The first method evaluated the data by measuring the percentage of artifacts. The second method tested if the expectation of the experimental results was fulfilled (i.e., if the target trials contained the P300 component). The validation proved that most datasets were suitable for subsequent analysis.
conclusionsThe presented datasets together with their metadata provide researchers with an opportunity to study the P300 component from different perspectives. Furthermore, they can be used for BCI research.
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Registered trials
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