Classification of motor imagery tasks by means of time-frequency-spatial analysis for Brain-Computer Interface applications

L. Qin; B. Kamousi; Z. M. Liu; L. Ding; Bin He

doi:10.1109/CNE.2005.1419636

Classification of motor imagery tasks by means of time-frequency-spatial analysis for Brain-Computer Interface applications

L. Qin, B. Kamousi, Z. M. Liu, L. Ding, Bin He

Biomedical Engineering

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

7 Scopus citations

Abstract

We have developed new algorithms for classification of motor imagery tasks for Brain-Computer Interface applications by analyzing single trial scalp EEG signals in the time-, frequency-, and space- domains. These new algorithms have been evaluated using a publically available dataset. The results are promising, suggesting that the newly developed algorithms may provide useful alternative for noninvaisve Brain-Computer Interface applications.

Original language	English (US)
Pages	374-376
Number of pages	3
DOIs	https://doi.org/10.1109/CNE.2005.1419636
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

Keywords

Brain-computer interface
EEG
Inverse solutions
Motor imagery
Spatial analysis
Time-frequency analysis

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10.1109/CNE.2005.1419636

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title = "Classification of motor imagery tasks by means of time-frequency-spatial analysis for Brain-Computer Interface applications",

abstract = "We have developed new algorithms for classification of motor imagery tasks for Brain-Computer Interface applications by analyzing single trial scalp EEG signals in the time-, frequency-, and space- domains. These new algorithms have been evaluated using a publically available dataset. The results are promising, suggesting that the newly developed algorithms may provide useful alternative for noninvaisve Brain-Computer Interface applications.",

keywords = "Brain-computer interface, EEG, Inverse solutions, Motor imagery, Spatial analysis, Time-frequency analysis",

author = "L. Qin and B. Kamousi and Liu, {Z. M.} and L. Ding and Bin He",

year = "2005",

doi = "10.1109/CNE.2005.1419636",

language = "English (US)",

pages = "374--376",

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

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AU - Qin, L.

AU - Kamousi, B.

AU - Liu, Z. M.

AU - Ding, L.

AU - He, Bin

PY - 2005

Y1 - 2005

N2 - We have developed new algorithms for classification of motor imagery tasks for Brain-Computer Interface applications by analyzing single trial scalp EEG signals in the time-, frequency-, and space- domains. These new algorithms have been evaluated using a publically available dataset. The results are promising, suggesting that the newly developed algorithms may provide useful alternative for noninvaisve Brain-Computer Interface applications.

AB - We have developed new algorithms for classification of motor imagery tasks for Brain-Computer Interface applications by analyzing single trial scalp EEG signals in the time-, frequency-, and space- domains. These new algorithms have been evaluated using a publically available dataset. The results are promising, suggesting that the newly developed algorithms may provide useful alternative for noninvaisve Brain-Computer Interface applications.

KW - Brain-computer interface

KW - EEG

KW - Inverse solutions

KW - Motor imagery

KW - Spatial analysis

KW - Time-frequency analysis

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DO - 10.1109/CNE.2005.1419636

M3 - Paper

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EP - 376

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

Y2 - 16 March 2005 through 19 March 2005

ER -

Classification of motor imagery tasks by means of time-frequency-spatial analysis for Brain-Computer Interface applications

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