Editorial: Datasets for Brain-Computer Interface Applications Article Swipe
Ian Daly
,
Ana Matran‐Fernandez
,
Davide Valeriani
,
Mikhail Lebedev
,
Andrea Kübler
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2021.732165
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3389/fnins.2021.732165
Montesano, & Müller-Putz, 2020). Ortega et al. collected a multimodal dataset comprising EEG, fNIRS, EMG, and movement data recorded during a force grip task (Ortega, Zhao, & Faisal, 2020).A wide range of other types of EEG-based BCIs are also presented. These include a dataset for a BCI based on covert attention shifts (Reichert et al., 2020) and an affective BCI based on neurofeedback (Charles, De Castro Martins, & Cavazza, 2020), as well as two BCIs based on the rapid serial visual presentation paradigm (S. Zhang, Wang, Zhang, & Gao, 2020;Zheng et al., 2020). The collection also includes a BCI for treating nicotine addiction via neurofeedback (Bu et al., 2021) and a dataset of SSVEP signals (Liu, Huang, Wang, Chen, & Gao, 2020).A diverse range of paradigms were used in this collection of studies. For example, Lühmann et al. present a resting state fNIRS dataset (von Lühmann, Li, Gilmore, Boas, & Yücel, 2020), while Parent and colleagues provide a multimodal dataset, comprising EEG, ECG, and respiration activity, recorded during a range of physical activities and induced stress . Finally, Albuquerque and colleagues offer a multimodal dataset, comprising EEG, ECG, and GSR, recorded during a mental workload paradigm .We hope the collected datasets will enable novel developments and applications of BCI technology, as well as extensive validation studies of current and future BCIs.
Related Topics
Concepts
Brain–computer interface
Neurofeedback
Electroencephalography
Computer science
Covert
Brain activity and meditation
Sensorimotor rhythm
Artificial intelligence
Motor imagery
Psychology
Neuroscience
Philosophy
Linguistics
Metadata
- Type
- editorial
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnins.2021.732165
- https://www.frontiersin.org/articles/10.3389/fnins.2021.732165/pdf
- OA Status
- gold
- Cited By
- 4
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3204697271
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3204697271Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fnins.2021.732165Digital Object Identifier
- Title
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Editorial: Datasets for Brain-Computer Interface ApplicationsWork title
- Type
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editorialOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-29Full publication date if available
- Authors
-
Ian Daly, Ana Matran‐Fernandez, Davide Valeriani, Mikhail Lebedev, Andrea KüblerList of authors in order
- Landing page
-
https://doi.org/10.3389/fnins.2021.732165Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fnins.2021.732165/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fnins.2021.732165/pdfDirect OA link when available
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Brain–computer interface, Neurofeedback, Electroencephalography, Computer science, Covert, Brain activity and meditation, Sensorimotor rhythm, Artificial intelligence, Motor imagery, Psychology, Neuroscience, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 2, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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15Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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