Improving the Accuracy and Training Speed of Motor Imagery Brain–Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors

In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain–computer interfaces. The proposed method is configured as follows: first, wavelet transforms are appli...

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Detalles Bibliográficos
Autores principales: Lee, David, Park, Sang-Hoon, Lee, Sang-Goog
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677306/
https://www.ncbi.nlm.nih.gov/pubmed/28991172
http://dx.doi.org/10.3390/s17102282

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677306/
https://www.ncbi.nlm.nih.gov/pubmed/28991172
http://dx.doi.org/10.3390/s17102282

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