Online impiementation of Top-Down SSVEP-BMI

Min Hee Ahn; Byoung-Kyong Min

doi:10.1109/IWW-BCI.2017.7858149

Online impiementation of Top-Down SSVEP-BMI

Min Hee Ahn, Byoung-Kyong Min

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

ßrain machine interfaces (BMls) enable us to control extern al devices using our brain signals. Using a grid-shaped flicke ring line-Array and a shrink-rLDA c1assifier, top-down information could recently be decoded in a steady-state visual evoked potential (SSVEP)-based BMI pamdigm. The present study tested its feasibility in online implementation. We found that within reasonable computing time (0.114 s on average) its online system was successfully accomplished with a decoding accuracy of 53.7% on average. The accuracy was 3.2 times significantly high er than the accuracy by mndom-shuffled data (16.7%). Therefore, using the grid-shaped SSVEP-based BMl, one's muIticlass (at least 6 c1asses) intention can be online decoded and subsequently control extemal devices.

Original language	English
Title of host publication	5th International Winter Conference on Brain-Computer Interface, BCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	27-29
Number of pages	3
ISBN (Electronic)	9781509050963
DOIs	https://doi.org/10.1109/IWW-BCI.2017.7858149
Publication status	Published - 2017 Feb 16
Event	5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of Duration: 2017 Jan 9 → 2017 Jan 11

Other

Other	5th International Winter Conference on Brain-Computer Interface, BCI 2017
Country/Territory	Korea, Republic of
City	Gangwon Province
Period	17/1/9 → 17/1/11

Keywords

Brain-machine interface
Component
Shrink rLDA
Top-Down SSVEP

ASJC Scopus subject areas

Signal Processing
Human-Computer Interaction

Access to Document

10.1109/IWW-BCI.2017.7858149

Cite this

Ahn, MH & Min, B-K 2017, Online impiementation of Top-Down SSVEP-BMI. in 5th International Winter Conference on Brain-Computer Interface, BCI 2017., 7858149, Institute of Electrical and Electronics Engineers Inc., pp. 27-29, 5th International Winter Conference on Brain-Computer Interface, BCI 2017, Gangwon Province, Korea, Republic of, 17/1/9. https://doi.org/10.1109/IWW-BCI.2017.7858149

@inproceedings{30ec643d84b5444a847d4ad8a0e14c29,

title = "Online impiementation of Top-Down SSVEP-BMI",

abstract = "{\ss}rain machine interfaces (BMls) enable us to control extern al devices using our brain signals. Using a grid-shaped flicke ring line-Array and a shrink-rLDA c1assifier, top-down information could recently be decoded in a steady-state visual evoked potential (SSVEP)-based BMI pamdigm. The present study tested its feasibility in online implementation. We found that within reasonable computing time (0.114 s on average) its online system was successfully accomplished with a decoding accuracy of 53.7% on average. The accuracy was 3.2 times significantly high er than the accuracy by mndom-shuffled data (16.7%). Therefore, using the grid-shaped SSVEP-based BMl, one's muIticlass (at least 6 c1asses) intention can be online decoded and subsequently control extemal devices.",

keywords = "Brain-machine interface, Component, Shrink rLDA, Top-Down SSVEP",

author = "Ahn, {Min Hee} and Byoung-Kyong Min",

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doi = "10.1109/IWW-BCI.2017.7858149",

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booktitle = "5th International Winter Conference on Brain-Computer Interface, BCI 2017",

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note = "5th International Winter Conference on Brain-Computer Interface, BCI 2017 ; Conference date: 09-01-2017 Through 11-01-2017",

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T1 - Online impiementation of Top-Down SSVEP-BMI

AU - Ahn, Min Hee

AU - Min, Byoung-Kyong

PY - 2017/2/16

Y1 - 2017/2/16

N2 - ßrain machine interfaces (BMls) enable us to control extern al devices using our brain signals. Using a grid-shaped flicke ring line-Array and a shrink-rLDA c1assifier, top-down information could recently be decoded in a steady-state visual evoked potential (SSVEP)-based BMI pamdigm. The present study tested its feasibility in online implementation. We found that within reasonable computing time (0.114 s on average) its online system was successfully accomplished with a decoding accuracy of 53.7% on average. The accuracy was 3.2 times significantly high er than the accuracy by mndom-shuffled data (16.7%). Therefore, using the grid-shaped SSVEP-based BMl, one's muIticlass (at least 6 c1asses) intention can be online decoded and subsequently control extemal devices.

AB - ßrain machine interfaces (BMls) enable us to control extern al devices using our brain signals. Using a grid-shaped flicke ring line-Array and a shrink-rLDA c1assifier, top-down information could recently be decoded in a steady-state visual evoked potential (SSVEP)-based BMI pamdigm. The present study tested its feasibility in online implementation. We found that within reasonable computing time (0.114 s on average) its online system was successfully accomplished with a decoding accuracy of 53.7% on average. The accuracy was 3.2 times significantly high er than the accuracy by mndom-shuffled data (16.7%). Therefore, using the grid-shaped SSVEP-based BMl, one's muIticlass (at least 6 c1asses) intention can be online decoded and subsequently control extemal devices.

KW - Brain-machine interface

KW - Component

KW - Shrink rLDA

KW - Top-Down SSVEP

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BT - 5th International Winter Conference on Brain-Computer Interface, BCI 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Winter Conference on Brain-Computer Interface, BCI 2017

Y2 - 9 January 2017 through 11 January 2017

ER -

Online impiementation of Top-Down SSVEP-BMI

Abstract

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Keywords

ASJC Scopus subject areas

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