FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2968287505> ?p ?o ?g. }

• W2968287505 abstract "Invasive brain-computer interfaces yield remarkable performance in a multitude of applications. For classification experiments, high-gamma bandpower features and linear discriminant analysis (LDA) are commonly used due to simplicity and robustness. However, LDA is inherently static and not suited to account for transient information that is typically present in high-gamma features. To resolve this issue, we here present an extension of LDA to the time-variant feature space. We call this method time-variant linear discriminant analysis (TVLDA). It intrinsically provides a feature reduction stage, which makes external approaches thereto obsolete, such as feature selection techniques or common spatial patterns (CSPs). As well, we propose a time-domain whitening stage which equalizes the pronounced 1/f-shape of the typical brain-wave spectrum. We evaluated our proposed architecture based on recordings from 15 epilepsy patients with temporarily implanted subdural grids, who participated in additional research experiments besides clinical treatment. The experiments featured two different motor tasks involving three high-level gestures and individual finger movement. We used log-transformed bandpower features from the high-gamma band (50-300 Hz, excluding power-line harmonics) for classification. On average, whitening improved the classification performance by about 11%. On whitened data, TVLDA outperformed LDA with feature selection by 11.8%, LDA with CSPs by 13.9%, and regularized LDA with vectorized features by 16.4%. At the same time, TVLDA only required one or two internal features to achieve this. TVLDA provides stable results even if very few trials are available. It is easy to implement, fully automatic and deterministic. Due to its low complexity, TVLDA is suited for real-time brain-computer interfaces. Training is done in less than a second. TVLDA performed particularly well in experiments with data from high-density electrode arrays. For example, the three high-level gestures were correctly identified at a rate of 99% over all subjects. Similarly, the decoding accuracy of individual fingers was 96% on average over all subjects. To our knowledge, these mean accuracies are the highest ever reported for three-class and five-class motor-control BCIs." @default.
• W2968287505 created "2019-08-22" @default.
• W2968287505 creator A5003444525 @default.
• W2968287505 creator A5007078334 @default.
• W2968287505 creator A5031377323 @default.
• W2968287505 creator A5067504073 @default.
• W2968287505 creator A5071455296 @default.
• W2968287505 creator A5090258284 @default.
• W2968287505 date "2019-09-26" @default.
• W2968287505 modified "2023-09-30" @default.
• W2968287505 title "Time-Variant Linear Discriminant Analysis Improves Hand Gesture and Finger Movement Decoding for Invasive Brain-Computer Interfaces" @default.
• W2968287505 cites W1545922961 @default.
• W2968287505 cites W1567383927 @default.
• W2968287505 cites W1936852542 @default.
• W2968287505 cites W1995255811 @default.
• W2968287505 cites W2011527020 @default.
• W2968287505 cites W2062614893 @default.
• W2968287505 cites W2068184837 @default.
• W2968287505 cites W2072253381 @default.
• W2968287505 cites W2073989793 @default.
• W2968287505 cites W2074133775 @default.
• W2968287505 cites W2075647286 @default.
• W2968287505 cites W2089318762 @default.
• W2968287505 cites W2094017272 @default.
• W2968287505 cites W2098844365 @default.
• W2968287505 cites W2099509424 @default.
• W2968287505 cites W2102800711 @default.
• W2968287505 cites W2106006415 @default.
• W2968287505 cites W2115383940 @default.
• W2968287505 cites W2116308679 @default.
• W2968287505 cites W2118030309 @default.
• W2968287505 cites W2119163516 @default.
• W2968287505 cites W2138465873 @default.
• W2968287505 cites W2141309775 @default.
• W2968287505 cites W2152171700 @default.
• W2968287505 cites W2152465745 @default.
• W2968287505 cites W2159318219 @default.
• W2968287505 cites W21769058 @default.
• W2968287505 cites W2273148818 @default.
• W2968287505 cites W2337492206 @default.
• W2968287505 cites W2498264865 @default.
• W2968287505 cites W2560473548 @default.
• W2968287505 cites W2568678377 @default.
• W2968287505 cites W2751551459 @default.
• W2968287505 cites W2791761603 @default.
• W2968287505 cites W2794345050 @default.
• W2968287505 cites W2888508477 @default.
• W2968287505 cites W2951881727 @default.
• W2968287505 doi "https://doi.org/10.3389/fnins.2019.00901" @default.
• W2968287505 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6775278" @default.
• W2968287505 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31616237" @default.
• W2968287505 hasPublicationYear "2019" @default.
• W2968287505 type Work @default.
• W2968287505 sameAs 2968287505 @default.
• W2968287505 citedByCount "20" @default.
• W2968287505 countsByYear W29682875052020 @default.
• W2968287505 countsByYear W29682875052021 @default.
• W2968287505 countsByYear W29682875052022 @default.
• W2968287505 countsByYear W29682875052023 @default.
• W2968287505 crossrefType "journal-article" @default.
• W2968287505 hasAuthorship W2968287505A5003444525 @default.
• W2968287505 hasAuthorship W2968287505A5007078334 @default.
• W2968287505 hasAuthorship W2968287505A5031377323 @default.
• W2968287505 hasAuthorship W2968287505A5067504073 @default.
• W2968287505 hasAuthorship W2968287505A5071455296 @default.
• W2968287505 hasAuthorship W2968287505A5090258284 @default.
• W2968287505 hasBestOaLocation W29682875051 @default.
• W2968287505 hasConcept C104317684 @default.
• W2968287505 hasConcept C138885662 @default.
• W2968287505 hasConcept C148483581 @default.
• W2968287505 hasConcept C153180895 @default.
• W2968287505 hasConcept C154945302 @default.
• W2968287505 hasConcept C185592680 @default.
• W2968287505 hasConcept C2776401178 @default.
• W2968287505 hasConcept C28490314 @default.
• W2968287505 hasConcept C41008148 @default.
• W2968287505 hasConcept C41895202 @default.
• W2968287505 hasConcept C52622490 @default.
• W2968287505 hasConcept C55493867 @default.
• W2968287505 hasConcept C63479239 @default.
• W2968287505 hasConcept C69738355 @default.
• W2968287505 hasConceptScore W2968287505C104317684 @default.
• W2968287505 hasConceptScore W2968287505C138885662 @default.
• W2968287505 hasConceptScore W2968287505C148483581 @default.
• W2968287505 hasConceptScore W2968287505C153180895 @default.
• W2968287505 hasConceptScore W2968287505C154945302 @default.
• W2968287505 hasConceptScore W2968287505C185592680 @default.
• W2968287505 hasConceptScore W2968287505C2776401178 @default.
• W2968287505 hasConceptScore W2968287505C28490314 @default.
• W2968287505 hasConceptScore W2968287505C41008148 @default.
• W2968287505 hasConceptScore W2968287505C41895202 @default.
• W2968287505 hasConceptScore W2968287505C52622490 @default.
• W2968287505 hasConceptScore W2968287505C55493867 @default.
• W2968287505 hasConceptScore W2968287505C63479239 @default.
• W2968287505 hasConceptScore W2968287505C69738355 @default.
• W2968287505 hasLocation W29682875051 @default.
• W2968287505 hasLocation W29682875052 @default.
• W2968287505 hasLocation W29682875053 @default.
• W2968287505 hasOpenAccess W2968287505 @default.
• W2968287505 hasPrimaryLocation W29682875051 @default.

• next