Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces

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

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

9 Scopus citations

Abstract

Steady-state visual evoked potential (SSVEP)-based brain computer-interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication. State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through a calibration process. However, due to notable human variability across individuals and within individuals over time, calibration (training) data collection is non-negligible and often laborious and time-consuming, deteriorating the practicality of SSVEP BCIs in a real-world context. This study aims to develop a cross-subject transferring approach to reduce the need for collecting training data from a test user with a newly proposed least-squares transformation (LST) method. Study results show the capability of the LST in reducing the number of training templates required for a 40-class SSVEP BCI. The LST method may lead to numerous real-world applications using near-zero-training/plug-and-play high-speed SSVEP BCIs.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages424-427
Number of pages4
ISBN (Electronic)9781538679210
DOIs
StatePublished - 16 May 2019
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: 20 Mar 201923 Mar 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
CountryUnited States
CitySan Francisco
Period20/03/1923/03/19

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