Exploring Human Variability in Steady-State Visual Evoked Potentials

Chun-Shu Wei, Masaki Nakanishi, Kuan Jung Chiang, Tzyy Ping Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

High-speed steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been developed to enable the communications between the human brain and external environments. One of the major issues in the real-world applications of SSVEP-BCIs is the laborious and time-consuming calibration process, triggering the development of transfer-learning approaches to leverage existing data from other users. A comprehensive investigation on the inter-and intra-subject variability in SSVEP data is thus needed to provide insight for designing future transfer-learning frameworks for SSVEP-BCIs. We hereby present the first study that systematically and quantitatively assesses the variability in SSVEP data, where the sources of inter-and intra-subject variability at low-and high-frequency range were identified using Fisher's discriminant ratios (FDRs). The insights gained from this work could drive the future developments of transfer-learning approaches to minimize the calibration efforts in high-speed SSVEP BCIs.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages474-479
Number of pages6
ISBN (Electronic)9781538666500
DOIs
StatePublished - 16 Jan 2019
Event2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018 - Miyazaki, Japan
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018

Conference

Conference2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
CountryJapan
CityMiyazaki
Period7/10/1810/10/18

Keywords

  • brain-computer interface (BCI)
  • Electroencephalogram (EEG)
  • Fisher's discriminant ratio (FDR)
  • steady-state visual evoked potential (SSVEP)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

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