How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection

Vinicio Changoluisa; Pablo Varona; Francisco B. Rodriguez

doi:10.1007/978-3-319-59147-6_42

How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection

Vinicio Changoluisa, Pablo Varona, Francisco B. Rodriguez

Electronics and Telematics Research Group (GIETEC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Currently, one of the challenges in a Brain Computer Interface (BCI) technologies is the improvement real-time event-related potential (ERP) detection. Variability and low signal-to-noise ratio (SNR) impair detection methods. We hypothesized that if in a P300-based BCI we find the electrodes with the maximum relative voltage area (the “maximum relative” term refers to the area within each trial, but not between trials) where a P300 can be located, we will improve the performance of a classifier and reduce the number of trials necessary to achieve 100% success. We propose a method that calculates successively the maximum relative voltage areas in the P300 region of the EEG signal for each stimulus. In this way, differences between a target and a non-target stimulus are maximized. This method was tested with a linear classifier (LDA), known for its good performance and low computational cost. We observed that a single electrode with maximum relative voltage area in a P300 region can give more information than the traditional 4 electrode measurement. The preliminary results show that by detecting appropriate characteristics in the EEG signal, we can reduce the error by trial as well as the number of electrodes. The detection of the maximum relative voltage area in the EEG electrodes is a characteristic that can contribute to increase the SNR and decrease the prediction error with the smallest number of trials in the P300-based BCI systems. This type of methods that seek specific characteristics in the signals can also contribute to the management of the variability present in the BCI systems. This method can be used both for an online and offline analysis.

Original language	English
Title of host publication	Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Proceedings
Editors	Ignacio Rojas, Andreu Catala, Gonzalo Joya
Publisher	Springer Verlag
Pages	486-497
Number of pages	12
ISBN (Print)	9783319591469
DOIs	https://doi.org/10.1007/978-3-319-59147-6_42
State	Published - 2017
Event	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 - Zakopane, Poland Duration: 11 Jun 2017 → 15 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10306 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017
Country/Territory	Poland
City	Zakopane
Period	11/06/17 → 15/06/17

Bibliographical note

Funding Information:
This work was funded by Spanish projects of Ministerio de Economía y Competitividad/FEDER TIN2014-54580-R, DPI2015-65833-P ( http://www.mineco.gob.es/ ) and Predoctoral Research Grants 2015-AR2Q9086 of the Government of Ecuador through the Secretaría de Educación Superior, Ciencia, Tecnología e Innovación (SENESCYT).

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Brain Computer Interface
Event-related potentials
LDA
Online
P300

Access to Document

10.1007/978-3-319-59147-6_42

Cite this

Changoluisa, V., Varona, P., & Rodriguez, F. B. (2017). How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection. In I. Rojas, A. Catala, & G. Joya (Eds.), Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Proceedings (pp. 486-497). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10306 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59147-6_42

Changoluisa, Vinicio ; Varona, Pablo ; Rodriguez, Francisco B. / How to reduce classification error in ERP-based BCI : Maximum relative areas as a feature for p300 detection. Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Proceedings. editor / Ignacio Rojas ; Andreu Catala ; Gonzalo Joya. Springer Verlag, 2017. pp. 486-497 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection",

abstract = "Currently, one of the challenges in a Brain Computer Interface (BCI) technologies is the improvement real-time event-related potential (ERP) detection. Variability and low signal-to-noise ratio (SNR) impair detection methods. We hypothesized that if in a P300-based BCI we find the electrodes with the maximum relative voltage area (the “maximum relative” term refers to the area within each trial, but not between trials) where a P300 can be located, we will improve the performance of a classifier and reduce the number of trials necessary to achieve 100% success. We propose a method that calculates successively the maximum relative voltage areas in the P300 region of the EEG signal for each stimulus. In this way, differences between a target and a non-target stimulus are maximized. This method was tested with a linear classifier (LDA), known for its good performance and low computational cost. We observed that a single electrode with maximum relative voltage area in a P300 region can give more information than the traditional 4 electrode measurement. The preliminary results show that by detecting appropriate characteristics in the EEG signal, we can reduce the error by trial as well as the number of electrodes. The detection of the maximum relative voltage area in the EEG electrodes is a characteristic that can contribute to increase the SNR and decrease the prediction error with the smallest number of trials in the P300-based BCI systems. This type of methods that seek specific characteristics in the signals can also contribute to the management of the variability present in the BCI systems. This method can be used both for an online and offline analysis.",

keywords = "Brain Computer Interface, Event-related potentials, LDA, Online, P300",

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Changoluisa, V, Varona, P & Rodriguez, FB 2017, How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection. in I Rojas, A Catala & G Joya (eds), Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10306 LNCS, Springer Verlag, pp. 486-497, 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, Zakopane, Poland, 11/06/17. https://doi.org/10.1007/978-3-319-59147-6_42

How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection. / Changoluisa, Vinicio; Varona, Pablo; Rodriguez, Francisco B.
Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Proceedings. ed. / Ignacio Rojas; Andreu Catala; Gonzalo Joya. Springer Verlag, 2017. p. 486-497 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10306 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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T2 - 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017

AU - Changoluisa, Vinicio

AU - Varona, Pablo

AU - Rodriguez, Francisco B.

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Currently, one of the challenges in a Brain Computer Interface (BCI) technologies is the improvement real-time event-related potential (ERP) detection. Variability and low signal-to-noise ratio (SNR) impair detection methods. We hypothesized that if in a P300-based BCI we find the electrodes with the maximum relative voltage area (the “maximum relative” term refers to the area within each trial, but not between trials) where a P300 can be located, we will improve the performance of a classifier and reduce the number of trials necessary to achieve 100% success. We propose a method that calculates successively the maximum relative voltage areas in the P300 region of the EEG signal for each stimulus. In this way, differences between a target and a non-target stimulus are maximized. This method was tested with a linear classifier (LDA), known for its good performance and low computational cost. We observed that a single electrode with maximum relative voltage area in a P300 region can give more information than the traditional 4 electrode measurement. The preliminary results show that by detecting appropriate characteristics in the EEG signal, we can reduce the error by trial as well as the number of electrodes. The detection of the maximum relative voltage area in the EEG electrodes is a characteristic that can contribute to increase the SNR and decrease the prediction error with the smallest number of trials in the P300-based BCI systems. This type of methods that seek specific characteristics in the signals can also contribute to the management of the variability present in the BCI systems. This method can be used both for an online and offline analysis.

AB - Currently, one of the challenges in a Brain Computer Interface (BCI) technologies is the improvement real-time event-related potential (ERP) detection. Variability and low signal-to-noise ratio (SNR) impair detection methods. We hypothesized that if in a P300-based BCI we find the electrodes with the maximum relative voltage area (the “maximum relative” term refers to the area within each trial, but not between trials) where a P300 can be located, we will improve the performance of a classifier and reduce the number of trials necessary to achieve 100% success. We propose a method that calculates successively the maximum relative voltage areas in the P300 region of the EEG signal for each stimulus. In this way, differences between a target and a non-target stimulus are maximized. This method was tested with a linear classifier (LDA), known for its good performance and low computational cost. We observed that a single electrode with maximum relative voltage area in a P300 region can give more information than the traditional 4 electrode measurement. The preliminary results show that by detecting appropriate characteristics in the EEG signal, we can reduce the error by trial as well as the number of electrodes. The detection of the maximum relative voltage area in the EEG electrodes is a characteristic that can contribute to increase the SNR and decrease the prediction error with the smallest number of trials in the P300-based BCI systems. This type of methods that seek specific characteristics in the signals can also contribute to the management of the variability present in the BCI systems. This method can be used both for an online and offline analysis.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Changoluisa V, Varona P, Rodriguez FB. How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection. In Rojas I, Catala A, Joya G, editors, Advances in Computational Intelligence - 14th International Work-Conference on Artificial Neural Networks, IWANN 2017, Proceedings. Springer Verlag. 2017. p. 486-497. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59147-6_42

How to reduce classification error in ERP-based BCI: Maximum relative areas as a feature for p300 detection

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