The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Performance variation in motor imagery brain–computer interface: A brief review
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Citations
Cites
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
Neurophysiological predictor of SMR-based BCI performance
Blankertz, B., Sannelli, C., Halder, S., Hammer, E. M., Kübler, A., Müller, K.-R., Curio, G., & Dickhaus, T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.022 · Cited by 674
BCI demographics: how many (and what kinds of) people can use an SSVEP BCI?
Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2039495 · Cited by 344
Nijboer (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00055 · Cited by 323
EEG-Based Brain-Computer Interfaces: A Thorough Literature Survey
Hwang, H.-J., Kim, S., Choi, S., & Im, C.-H. (2013) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2013.780869 · Cited by 257
Machine-learning-based coadaptive calibration for brain-computer interfaces
Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011) · Neural Computation · doi:10.1162/neco_a_00089 · Cited by 207
How Many People Could Use an SSVEP BCI?
Guger, C., Allison, B. Z., Großwindhager, B., Prückl, R., Hintermüller, C., Kapeller, C., Bruckner, M., Krausz, G., & Edlinger, G. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00169 · Cited by 176
Cited by
A novel deep learning approach for classification of EEG motor imagery signals
Tabar, Y. R., & Halici, U. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2560/14/1/016003 · Cited by 859
Progress in Brain Computer Interface: Challenges and Opportunities
Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khandoker, A. H., & Baumert, M. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.578875 · Cited by 365
Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review
Saha, S., & Baumert, M. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00087 · Cited by 325
Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces
Hong, K.-S., Khan, M. J., & Hong, M. J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00246 · Cited by 283
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296
Metadata
- Authors
- Ahn, M., & Jun, S. C.
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2015-03
- Volume
- 243
- Pages
- 103-110
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W1968417898
- Cited by
- 316
- References
- 8
- FWCI
- 13.64
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Ahn, M., & Jun, S. C. (2015). Performance variation in motor imagery brain–computer interface: A brief review. Journal of Neuroscience Methods. https://doi.org/10.1016/j.jneumeth.2015.01.033
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
