Robust classification of EEG signal for brain-computer interface
We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller us...
Saved in:
Main Authors: | , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2006
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/3491 https://ink.library.smu.edu.sg/context/sis_research/article/4492/viewcontent/RobustClassificationEEGSignal_2006_TNSRE.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-4492 |
---|---|
record_format |
dspace |
spelling |
sg-smu-ink.sis_research-44922017-08-24T04:35:27Z Robust classification of EEG signal for brain-computer interface THULASIDAS, Manoj GUAN, Cuntai WU, Jiankang We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller. 2006-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3491 info:doi/10.1109/TNSRE.2005.862695 https://ink.library.smu.edu.sg/context/sis_research/article/4492/viewcontent/RobustClassificationEEGSignal_2006_TNSRE.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University P300 brain-computer interface event related potential speller support vector machine (SVM) Computer Sciences Graphics and Human Computer Interfaces |
institution |
Singapore Management University |
building |
SMU Libraries |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
SMU Libraries |
collection |
InK@SMU |
language |
English |
topic |
P300 brain-computer interface event related potential speller support vector machine (SVM) Computer Sciences Graphics and Human Computer Interfaces |
spellingShingle |
P300 brain-computer interface event related potential speller support vector machine (SVM) Computer Sciences Graphics and Human Computer Interfaces THULASIDAS, Manoj GUAN, Cuntai WU, Jiankang Robust classification of EEG signal for brain-computer interface |
description |
We report the implementation of a text input application (speller) based on the P300 event related potential. We obtain high accuracies by using an SVM classifier and a novel feature. These techniques enable us to maintain fast performance without sacrificing the accuracy, thus making the speller usable in an online mode. In order to further improve the usability, we perform various studies on the data with a view to minimizing the training time required. We present data collected from nine healthy subjects, along with the high accuracies (of the order of 95% or more) measured online. We show that the training time can be further reduced by a factor of two from its current value of about 20 min. High accuracy, fast learning, and online performance make this P300 speller a potential communication tool for severely disabled individuals, who have lost all other means of communication and are otherwise cut off from the world, provided their disability does not interfere with the performance of the speller. |
format |
text |
author |
THULASIDAS, Manoj GUAN, Cuntai WU, Jiankang |
author_facet |
THULASIDAS, Manoj GUAN, Cuntai WU, Jiankang |
author_sort |
THULASIDAS, Manoj |
title |
Robust classification of EEG signal for brain-computer interface |
title_short |
Robust classification of EEG signal for brain-computer interface |
title_full |
Robust classification of EEG signal for brain-computer interface |
title_fullStr |
Robust classification of EEG signal for brain-computer interface |
title_full_unstemmed |
Robust classification of EEG signal for brain-computer interface |
title_sort |
robust classification of eeg signal for brain-computer interface |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2006 |
url |
https://ink.library.smu.edu.sg/sis_research/3491 https://ink.library.smu.edu.sg/context/sis_research/article/4492/viewcontent/RobustClassificationEEGSignal_2006_TNSRE.pdf |
_version_ |
1770573233914904576 |