TY - GEN
T1 - Exploring Human Variability in Steady-State Visual Evoked Potentials
AU - Wei, Chun-Shu
AU - Nakanishi, Masaki
AU - Chiang, Kuan Jung
AU - Jung, Tzyy Ping
PY - 2019/1/16
Y1 - 2019/1/16
N2 - 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.
AB - 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.
KW - brain-computer interface (BCI)
KW - Electroencephalogram (EEG)
KW - Fisher's discriminant ratio (FDR)
KW - steady-state visual evoked potential (SSVEP)
KW - t-Distributed Stochastic Neighbor Embedding (t-SNE)
UR - http://www.scopus.com/inward/record.url?scp=85062213455&partnerID=8YFLogxK
U2 - 10.1109/SMC.2018.00090
DO - 10.1109/SMC.2018.00090
M3 - Conference contribution
AN - SCOPUS:85062213455
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 474
EP - 479
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -
