Time-Variant Linear Discriminant Analysis Improves Hand Gesture and Finger Movement Decoding for Invasive Brain-Computer Interfaces.
brain-computer interface
electrocorticography
linear discriminant analysis
movement decoding
spectral whitening
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2019
2019
Historique:
received:
28
02
2019
accepted:
12
08
2019
entrez:
17
10
2019
pubmed:
17
10
2019
medline:
17
10
2019
Statut:
epublish
Résumé
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
Identifiants
pubmed: 31616237
doi: 10.3389/fnins.2019.00901
pmc: PMC6775278
doi:
Types de publication
Journal Article
Langues
eng
Pagination
901Informations de copyright
Copyright © 2019 Gruenwald, Znobishchev, Kapeller, Kamada, Scharinger and Guger.
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