Please use this identifier to cite or link to this item: https://doi.org/10.1109/NER.2013.6696024
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dc.titleA combination of spatial and spectral filters for mental condition discrimination
dc.contributor.authorYu, K.
dc.contributor.authorShen, K.
dc.contributor.authorLi, X.
dc.date.accessioned2014-10-07T09:12:56Z
dc.date.available2014-10-07T09:12:56Z
dc.date.issued2013
dc.identifier.citationYu, K.,Shen, K.,Li, X. (2013). A combination of spatial and spectral filters for mental condition discrimination. International IEEE/EMBS Conference on Neural Engineering, NER : 673-676. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/NER.2013.6696024" target="_blank">https://doi.org/10.1109/NER.2013.6696024</a>
dc.identifier.isbn9781467319690
dc.identifier.issn19483546
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/85850
dc.description.abstractIt is widely accepted that the common spatial pattern (CSP) analysis method, albeit being very popular in brain-computer interface (BCI) applications as a feature extraction method for binary classification, is vulnerable to artifact. It could underperform when it is exposed to an input whose frequency band is too broad that many interfering frequency components are contained. These drawbacks are closely related to the nature of CSP filters which are based on completely spatial weighting. That is, CSP has no control on the temporal space of brain signals. This work is one attempt to extend CSP by eliminating the undesirable temporal components through spectral filtering. The proposed method in this work retains the simplicity of CSP but derives a number of complex spatial and spectral integrated filters by applying multiple time lags and a regularization term. These filters are data-driven and channel-specific. Their ability to narrow the frequency band of signals so as to enhance feature extraction is demonstrated using a public available dataset, where 4.7% higher mean classification accuracy is achieved. © 2013 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/NER.2013.6696024
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentMECHANICAL ENGINEERING
dc.description.doi10.1109/NER.2013.6696024
dc.description.sourcetitleInternational IEEE/EMBS Conference on Neural Engineering, NER
dc.description.page673-676
dc.identifier.isiutNOT_IN_WOS
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