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
PublisherSpringer
Pages107-118
Number of pages12
Volume151
Edition1
ISBN (Electronic)978-981-13-8950-4
ISBN (Print)978-981-13-8949-8
DOIs
Publication statusPublished - 2020

Publication series

NameSmart Innovation, Systems and Technologies
Volume151
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Keywords / Materials (for Non-textual outputs)

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

Fingerprint

Dive into the research topics of 'Dimension Reduction Techniques in a Brain–Computer Interface Application'. Together they form a unique fingerprint.

Cite this