Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems

Tuan Hoang, Dat Tran, Truoung Khoa, Trung Le, Xu Huang, Dharmendra Sharma, Toi Van

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

We investigate time domain parameters called high order moments in functional Near Infrared Spectroscopy (fNIRS) signal and propose to use them as new brain features in fNIRS-based Brain Computer Interface (BCI) research. These high order moments are well appropriate with fNIRS data without any special preprocessing or filtering step. Therefore, they could be used to guide users in feedback fNIRS-based BCI experiments. We performed experiments on motor imagery and person identification problems with the 2nd order moment, 4th order moment and a combination of these moments. Experimental results showed that these features provided high accuracy. For motor imagery problem, our system could achieve accuracy up to 99.5% for subject independent problem and varies between 86.5±5.4% and 97.0±2.1% for subject dependent problem. For person identification problem, our system could achieve accuracy nearly 100%. Comparing with other systems that used non-filtered raw signal as feature, these features are more stable than the raw signal because of noise reduction. We also found that the 2nd order moment alone could be an excellent and efficient feature for fNIRS-based BCI systems.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP 2012)
Subtitle of host publicationLecture Notes in Computer Science
EditorsTingwen Huang, Zhigang Zeng, Chuandong Li, Chi Sing Leung
Place of PublicationGermany
PublisherSpringer Verlag
Pages192-201
Number of pages10
Volume7664
ISBN (Electronic)9783642344817
ISBN (Print)9783642344800
DOIs
Publication statusPublished - 2012
Event19th International Conference on Neural Information Processing 2012 - Doha, Doha, Qatar
Duration: 12 Nov 201215 Nov 2012

Conference

Conference19th International Conference on Neural Information Processing 2012
CountryQatar
CityDoha
Period12/11/1215/11/12

Fingerprint

Brain computer interface
Near infrared spectroscopy
Feedback
Noise abatement
Brain
Experiments

Cite this

Hoang, T., Tran, D., Khoa, T., Le, T., Huang, X., Sharma, D., & Van, T. (2012). Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems. In T. Huang, Z. Zeng, C. Li, & C. S. Leung (Eds.), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science (Vol. 7664, pp. 192-201). Germany: Springer Verlag. https://doi.org/10.1007/978-3-642-34481-7_24
Hoang, Tuan ; Tran, Dat ; Khoa, Truoung ; Le, Trung ; Huang, Xu ; Sharma, Dharmendra ; Van, Toi. / Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems. International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. editor / Tingwen Huang ; Zhigang Zeng ; Chuandong Li ; Chi Sing Leung. Vol. 7664 Germany : Springer Verlag, 2012. pp. 192-201
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title = "Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems",
abstract = "We investigate time domain parameters called high order moments in functional Near Infrared Spectroscopy (fNIRS) signal and propose to use them as new brain features in fNIRS-based Brain Computer Interface (BCI) research. These high order moments are well appropriate with fNIRS data without any special preprocessing or filtering step. Therefore, they could be used to guide users in feedback fNIRS-based BCI experiments. We performed experiments on motor imagery and person identification problems with the 2nd order moment, 4th order moment and a combination of these moments. Experimental results showed that these features provided high accuracy. For motor imagery problem, our system could achieve accuracy up to 99.5{\%} for subject independent problem and varies between 86.5±5.4{\%} and 97.0±2.1{\%} for subject dependent problem. For person identification problem, our system could achieve accuracy nearly 100{\%}. Comparing with other systems that used non-filtered raw signal as feature, these features are more stable than the raw signal because of noise reduction. We also found that the 2nd order moment alone could be an excellent and efficient feature for fNIRS-based BCI systems.",
keywords = "Brain-Computer Interface, fNIRS, High order moment, Motor Imagery, Person identification, Hjorth parameters",
author = "Tuan Hoang and Dat Tran and Truoung Khoa and Trung Le and Xu Huang and Dharmendra Sharma and Toi Van",
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Hoang, T, Tran, D, Khoa, T, Le, T, Huang, X, Sharma, D & Van, T 2012, Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems. in T Huang, Z Zeng, C Li & CS Leung (eds), International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. vol. 7664, Springer Verlag, Germany, pp. 192-201, 19th International Conference on Neural Information Processing 2012, Doha, Qatar, 12/11/12. https://doi.org/10.1007/978-3-642-34481-7_24

Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems. / Hoang, Tuan; Tran, Dat; Khoa, Truoung; Le, Trung; Huang, Xu; Sharma, Dharmendra; Van, Toi.

International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. ed. / Tingwen Huang; Zhigang Zeng; Chuandong Li; Chi Sing Leung. Vol. 7664 Germany : Springer Verlag, 2012. p. 192-201.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems

AU - Hoang, Tuan

AU - Tran, Dat

AU - Khoa, Truoung

AU - Le, Trung

AU - Huang, Xu

AU - Sharma, Dharmendra

AU - Van, Toi

PY - 2012

Y1 - 2012

N2 - We investigate time domain parameters called high order moments in functional Near Infrared Spectroscopy (fNIRS) signal and propose to use them as new brain features in fNIRS-based Brain Computer Interface (BCI) research. These high order moments are well appropriate with fNIRS data without any special preprocessing or filtering step. Therefore, they could be used to guide users in feedback fNIRS-based BCI experiments. We performed experiments on motor imagery and person identification problems with the 2nd order moment, 4th order moment and a combination of these moments. Experimental results showed that these features provided high accuracy. For motor imagery problem, our system could achieve accuracy up to 99.5% for subject independent problem and varies between 86.5±5.4% and 97.0±2.1% for subject dependent problem. For person identification problem, our system could achieve accuracy nearly 100%. Comparing with other systems that used non-filtered raw signal as feature, these features are more stable than the raw signal because of noise reduction. We also found that the 2nd order moment alone could be an excellent and efficient feature for fNIRS-based BCI systems.

AB - We investigate time domain parameters called high order moments in functional Near Infrared Spectroscopy (fNIRS) signal and propose to use them as new brain features in fNIRS-based Brain Computer Interface (BCI) research. These high order moments are well appropriate with fNIRS data without any special preprocessing or filtering step. Therefore, they could be used to guide users in feedback fNIRS-based BCI experiments. We performed experiments on motor imagery and person identification problems with the 2nd order moment, 4th order moment and a combination of these moments. Experimental results showed that these features provided high accuracy. For motor imagery problem, our system could achieve accuracy up to 99.5% for subject independent problem and varies between 86.5±5.4% and 97.0±2.1% for subject dependent problem. For person identification problem, our system could achieve accuracy nearly 100%. Comparing with other systems that used non-filtered raw signal as feature, these features are more stable than the raw signal because of noise reduction. We also found that the 2nd order moment alone could be an excellent and efficient feature for fNIRS-based BCI systems.

KW - Brain-Computer Interface

KW - fNIRS

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KW - Motor Imagery

KW - Person identification

KW - Hjorth parameters

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DO - 10.1007/978-3-642-34481-7_24

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SN - 9783642344800

VL - 7664

SP - 192

EP - 201

BT - International Conference on Neural Information Processing (ICONIP 2012)

A2 - Huang, Tingwen

A2 - Zeng, Zhigang

A2 - Li, Chuandong

A2 - Leung, Chi Sing

PB - Springer Verlag

CY - Germany

ER -

Hoang T, Tran D, Khoa T, Le T, Huang X, Sharma D et al. Time Domain Parameters for Online Feedback fNIRS-Based Brain-Computer Interface Systems. In Huang T, Zeng Z, Li C, Leung CS, editors, International Conference on Neural Information Processing (ICONIP 2012): Lecture Notes in Computer Science. Vol. 7664. Germany: Springer Verlag. 2012. p. 192-201 https://doi.org/10.1007/978-3-642-34481-7_24