Dimension Reduction Techniques in a Brain–Computer Interface Application

Federico Cozza, Paola Galdi, Angela Serra, Gabriele Pasqua, Luigi Pavone, Roberto Tagliaferri*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract / Description of output

Electroencephalography (EEG)-based Brain–computer interface (BCI) technology allows a user to control an external device without muscle intervention through recorded neural activity. Ongoing research on BCI systems includes applications in the medical field to assist subjects with impaired motor functionality (e.g., for the control of prosthetic devices). In this context, the accuracy and efficiency of a BCI system are of paramount importance. Comparing four different dimension reduction techniques in combination with linear and nonlinear classifiers, we show that integrating these methods in a BCI system results in a reduced model complexity without affecting overall accuracy.

Original languageEnglish
Title of host publicationNeural Approaches to Dynamics of Signal Exchanges
EditorsAnna Esposito, Marcos Faundez-Zanuy, Francesco Carlo Morabito, Eros Pasero
Number of pages12
ISBN (Electronic)978-981-13-8950-4
ISBN (Print)978-981-13-8949-8
Publication statusPublished - 2020

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Keywords / Materials (for Non-textual outputs)

  • Brain–computer interface
  • Dimension reduction
  • EEG
  • Machine learning
  • p300


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