Enhancement of performance of an EEG-based brain-computer interface by means of a Time-Frequency approach

N. Yamawaki; Christopher T Wilke; Z. M. Liu; Bin He

doi:10.1109/CNE.2005.1419561

Enhancement of performance of an EEG-based brain-computer interface by means of a Time-Frequency approach

N. Yamawaki, Christopher T Wilke, Z. M. Liu, Bin He

Research output: Contribution to conference › Paper › peer-review

1 Scopus citations

Abstract

We have developed a novel Time-Frequency approach of classification of motor imagery (MI) tasks for brain-computer interface (BCI) applications. Through off-line data analysis on data collected during a "cursor control" experiment, we evaluated the capability of our proposed method in revealing the major features of the EEG control and enhancing MI classification accuracy. The pilot results in two human subjects are promising, with a mean accuracy rate of 87.9%, suggesting the feasibility of defining a new and more reliable EEG-based BCI.

Original language	English (US)
Pages	94-96
Number of pages	3
DOIs	https://doi.org/10.1109/CNE.2005.1419561
State	Published - 2005
Event	2nd International IEEE EMBS Conference on Neural Engineering, 2005 - Arlington, VA, United States Duration: Mar 16 2005 → Mar 19 2005

Other

Other	2nd International IEEE EMBS Conference on Neural Engineering, 2005
Country/Territory	United States
City	Arlington, VA
Period	3/16/05 → 3/19/05

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10.1109/CNE.2005.1419561

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title = "Enhancement of performance of an EEG-based brain-computer interface by means of a Time-Frequency approach",

abstract = "We have developed a novel Time-Frequency approach of classification of motor imagery (MI) tasks for brain-computer interface (BCI) applications. Through off-line data analysis on data collected during a {"}cursor control{"} experiment, we evaluated the capability of our proposed method in revealing the major features of the EEG control and enhancing MI classification accuracy. The pilot results in two human subjects are promising, with a mean accuracy rate of 87.9%, suggesting the feasibility of defining a new and more reliable EEG-based BCI.",

author = "N. Yamawaki and Wilke, {Christopher T} and Liu, {Z. M.} and Bin He",

year = "2005",

doi = "10.1109/CNE.2005.1419561",

language = "English (US)",

pages = "94--96",

note = "2nd International IEEE EMBS Conference on Neural Engineering, 2005 ; Conference date: 16-03-2005 Through 19-03-2005",

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TY - CONF

T1 - Enhancement of performance of an EEG-based brain-computer interface by means of a Time-Frequency approach

AU - Yamawaki, N.

AU - Wilke, Christopher T

AU - Liu, Z. M.

AU - He, Bin

PY - 2005

Y1 - 2005

N2 - We have developed a novel Time-Frequency approach of classification of motor imagery (MI) tasks for brain-computer interface (BCI) applications. Through off-line data analysis on data collected during a "cursor control" experiment, we evaluated the capability of our proposed method in revealing the major features of the EEG control and enhancing MI classification accuracy. The pilot results in two human subjects are promising, with a mean accuracy rate of 87.9%, suggesting the feasibility of defining a new and more reliable EEG-based BCI.

AB - We have developed a novel Time-Frequency approach of classification of motor imagery (MI) tasks for brain-computer interface (BCI) applications. Through off-line data analysis on data collected during a "cursor control" experiment, we evaluated the capability of our proposed method in revealing the major features of the EEG control and enhancing MI classification accuracy. The pilot results in two human subjects are promising, with a mean accuracy rate of 87.9%, suggesting the feasibility of defining a new and more reliable EEG-based BCI.

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U2 - 10.1109/CNE.2005.1419561

DO - 10.1109/CNE.2005.1419561

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T2 - 2nd International IEEE EMBS Conference on Neural Engineering, 2005

Y2 - 16 March 2005 through 19 March 2005

ER -

Enhancement of performance of an EEG-based brain-computer interface by means of a Time-Frequency approach

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