Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

63.130 UPC academic works
You are here:
View Item 
  •   DSpace Home
  • Treballs acadèmics
  • Màsters oficials
  • Master in Artificial Intelligence - MAI
  • View Item
  •   DSpace Home
  • Treballs acadèmics
  • Màsters oficials
  • Master in Artificial Intelligence - MAI
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A framework for artificial data generation based on anatomical differences for electroencephalography-based brain-computer interfaces

Thumbnail
View/Open
143659.pdf (10,49Mb)
Share:
 
  View Usage Statistics
Cita com:
hdl:2117/167803

Show full item record
Rapp, Rachel Elizabeth
Tutor / directorVellido Alcacena, AlfredoMés informacióMés informacióMés informació; Blankertz, Benjamin
CovenanteeTechnische Universität Berlin
Document typeMaster thesis
Date2019-07-01
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
One of the major limitations of brain-computer interfaces (BCI) is the need for a long and tedious calibration period in order for a subject to become proficient with the system. A principal challenge in training a BCI classifier that should work without user-specific calibration is that the training set is not large enough to capture the spectrum of potential signals. In this thesis, a new method to reduce BCI calibration time is proposed. Since one cause for subject-to-subject variability is the anatomical differences between subjects, we aimed to generate artificial data which would resemble the signals obtained from a new subject with a different cortical anatomy. This would allow for a large expansion of the training set size. To generate the artificial data we begin by decomposing the original signals, localizing the most prominent sources and shifting their orientation relative to the cortex. New signals are then regenerated using different head models. Training a classifier on the enriched training set should result in better generalizability. Although inter-subject classification ultimately fell outside the scope of this thesis, we consider intra-subject classification as a starting point for consideration of the methods applied. This ultimately lays the foundation for a much greater field of research involving the use of artificial data generation to combat the calibration time issue for BCIs.
SubjectsBrain-computer interfaces, Neural networks (Computer science), Interfícies cervell-ordinador, Xarxes neuronals (Informàtica)
DegreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2017)
URIhttp://hdl.handle.net/2117/167803
Collections
  • Màsters oficials - Master in Artificial Intelligence - MAI [239]
Share:
 
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
143659.pdf10,49MbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Inici de la pàgina