Analysing the robust EEG channel set for person authentication

Salahiddin Altahat, Girija CHETTY, Dat TRAN, Wanli MA

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

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

In this paper, we present the findings on the EEG channel selection and its impact on the robustness for EEG based person authentication. We test the effect of the enhancement threshold value (T_e), EEG frequency rhythms, mental task and the person identity on the selected EEG channels. Experimental validation of the work with publicly available EEG dataset, showed that the idle mental task provides the highest accuracy rates compared to other considered mental tasks. Moreover, we noticed that imaginary movement tasks provide better accuracy than actual movement tasks. Also for the frequency rhythm effect, the combined frequency rhythms increase the authentication accuracy better than using a single rhythm, so no single rhythm contains all the related identity information. Also for the T_e value, we found that the less T_e we consider, the more EEG channels to be included. Further, for the final part of this work, we tested if the selected channel are person specific. As a result, we found that EEG channel set, if selected for each person differently does enhance the authentication accuracy.
Original languageEnglish
Title of host publicationInternational Conference on Neural Information Processing (ICONIP)
Subtitle of host publicationLecture Notes in Computer Science (LNCS)
EditorsSabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu
Place of PublicationIstanbul
PublisherSpringer
Pages162-173
Number of pages12
Volume9492
ISBN (Print)9783319265605
DOIs
Publication statusPublished - 2015
Event22nd International Conference on Neural Information Processing ICONIP 2015 - Istanbul, Istanbul, Turkey
Duration: 9 Nov 201512 Nov 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9492
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Neural Information Processing ICONIP 2015
Abbreviated titleICONIP 2015
CountryTurkey
CityIstanbul
Period9/11/1512/11/15

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Electroencephalography
Authentication

Cite this

Altahat, S., CHETTY, G., TRAN, D., & MA, W. (2015). Analysing the robust EEG channel set for person authentication. In S. Arik, T. Huang, W. K. Lai, & Q. Liu (Eds.), International Conference on Neural Information Processing (ICONIP): Lecture Notes in Computer Science (LNCS) (Vol. 9492, pp. 162-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492). Istanbul: Springer. https://doi.org/10.1007/978-3-319-26561-2_20
Altahat, Salahiddin ; CHETTY, Girija ; TRAN, Dat ; MA, Wanli. / Analysing the robust EEG channel set for person authentication. International Conference on Neural Information Processing (ICONIP): Lecture Notes in Computer Science (LNCS). editor / Sabri Arik ; Tingwen Huang ; Weng Kin Lai ; Qingshan Liu. Vol. 9492 Istanbul : Springer, 2015. pp. 162-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "In this paper, we present the findings on the EEG channel selection and its impact on the robustness for EEG based person authentication. We test the effect of the enhancement threshold value (T_e), EEG frequency rhythms, mental task and the person identity on the selected EEG channels. Experimental validation of the work with publicly available EEG dataset, showed that the idle mental task provides the highest accuracy rates compared to other considered mental tasks. Moreover, we noticed that imaginary movement tasks provide better accuracy than actual movement tasks. Also for the frequency rhythm effect, the combined frequency rhythms increase the authentication accuracy better than using a single rhythm, so no single rhythm contains all the related identity information. Also for the T_e value, we found that the less T_e we consider, the more EEG channels to be included. Further, for the final part of this work, we tested if the selected channel are person specific. As a result, we found that EEG channel set, if selected for each person differently does enhance the authentication accuracy.",
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Altahat, S, CHETTY, G, TRAN, D & MA, W 2015, Analysing the robust EEG channel set for person authentication. in S Arik, T Huang, WK Lai & Q Liu (eds), International Conference on Neural Information Processing (ICONIP): Lecture Notes in Computer Science (LNCS). vol. 9492, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9492, Springer, Istanbul, pp. 162-173, 22nd International Conference on Neural Information Processing ICONIP 2015, Istanbul, Turkey, 9/11/15. https://doi.org/10.1007/978-3-319-26561-2_20

Analysing the robust EEG channel set for person authentication. / Altahat, Salahiddin; CHETTY, Girija; TRAN, Dat; MA, Wanli.

International Conference on Neural Information Processing (ICONIP): Lecture Notes in Computer Science (LNCS). ed. / Sabri Arik; Tingwen Huang; Weng Kin Lai; Qingshan Liu. Vol. 9492 Istanbul : Springer, 2015. p. 162-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9492).

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

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N2 - In this paper, we present the findings on the EEG channel selection and its impact on the robustness for EEG based person authentication. We test the effect of the enhancement threshold value (T_e), EEG frequency rhythms, mental task and the person identity on the selected EEG channels. Experimental validation of the work with publicly available EEG dataset, showed that the idle mental task provides the highest accuracy rates compared to other considered mental tasks. Moreover, we noticed that imaginary movement tasks provide better accuracy than actual movement tasks. Also for the frequency rhythm effect, the combined frequency rhythms increase the authentication accuracy better than using a single rhythm, so no single rhythm contains all the related identity information. Also for the T_e value, we found that the less T_e we consider, the more EEG channels to be included. Further, for the final part of this work, we tested if the selected channel are person specific. As a result, we found that EEG channel set, if selected for each person differently does enhance the authentication accuracy.

AB - In this paper, we present the findings on the EEG channel selection and its impact on the robustness for EEG based person authentication. We test the effect of the enhancement threshold value (T_e), EEG frequency rhythms, mental task and the person identity on the selected EEG channels. Experimental validation of the work with publicly available EEG dataset, showed that the idle mental task provides the highest accuracy rates compared to other considered mental tasks. Moreover, we noticed that imaginary movement tasks provide better accuracy than actual movement tasks. Also for the frequency rhythm effect, the combined frequency rhythms increase the authentication accuracy better than using a single rhythm, so no single rhythm contains all the related identity information. Also for the T_e value, we found that the less T_e we consider, the more EEG channels to be included. Further, for the final part of this work, we tested if the selected channel are person specific. As a result, we found that EEG channel set, if selected for each person differently does enhance the authentication accuracy.

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Altahat S, CHETTY G, TRAN D, MA W. Analysing the robust EEG channel set for person authentication. In Arik S, Huang T, Lai WK, Liu Q, editors, International Conference on Neural Information Processing (ICONIP): Lecture Notes in Computer Science (LNCS). Vol. 9492. Istanbul: Springer. 2015. p. 162-173. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-26561-2_20