English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/170643
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

AuthorsDelisle-Rodriguez, Denis; Villa-Parra, Ana; Bastos-Filho, Teodiano; López-Delis, Alberto; Frizera Neto, Anselmo ; Krishnan, Sridhar; Rocón, Eduardo
KeywordsSSVEP
artifact reduction
gait planning
feature selection
EEG
Laplacian
brain-computer interface
EOG
spatial filter
Issue Date2017
PublisherMolecular Diversity Preservation International
CitationSensors 17: 2725 (2017)
AbstractThis work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p < 0.01) improved for most of the subjects (ACC ¿ 74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank - Common Spatial Pattern, and Riemannian Geometry.
URIhttp://hdl.handle.net/10261/170643
DOI10.3390/s17122725
ISSN1424-8220
Appears in Collections:(CAR) Artículos
Files in This Item:
File Description SizeFormat 
sensors-17-02725-v3.pdf5,89 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.