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
    @inproceedings{49e84688f09245eb95abf4f65f1eadd1,
    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",
    year = "2012",
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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.

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    KW - fNIRS

    KW - High order moment

    KW - Motor Imagery

    KW - Person identification

    KW - Hjorth parameters

    U2 - 10.1007/978-3-642-34481-7_24

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    M3 - Conference contribution

    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