Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface
Journal of Neuroscience Methods, ISSN: 0165-0270, Vol: 255, Page: 85-91
2015
- 238Citations
- 196Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations238
- Citation Indexes238
- 238
- CrossRef223
- Captures196
- Readers196
- 196
Article Description
Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain–computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S016502701500285X; http://dx.doi.org/10.1016/j.jneumeth.2015.08.004; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84940043308&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/26277421; https://linkinghub.elsevier.com/retrieve/pii/S016502701500285X
Elsevier BV
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