P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies

Vanessa Salazar; Vinicio Changoluisa; Francisco B. Rodriguez

doi:10.1007/978-3-030-85030-2_21

P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies

Vanessa Salazar, Vinicio Changoluisa, Francisco B. Rodriguez

Electronics and Telematics Research Group (GIETEC)

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

Abstract

The analysis of connectivity in brain networks has been widely researched and it has been shown that certain cognitive processes require the integration of distributed brain areas. Functional connectivity attempts to statistically quantify the interdependence between these brain areas. In this paper, we propose an analysis of functional connectivity in the Event-Related Potential (ERP) context, more specifically on the P300 component using the Granger Causality measure. To this end, we propose a methodology that consists in quantifying the causality in the P300 and non-P300 signals in the context of Brain-Computer Interfaces (BCIs). Causality is calculated using two approaches: i) using standard electrodes and, ii) using electrodes selected using Bayesian Linear Discriminant Analysis and sequential forward electrode selection (BLDA-FS). Based on this analysis, it is shown that the Granger Causality metric is valid to show a significant connectivity difference between P300 and non-P300 signals. The electrodes selected using BLDA-FS were found to be more discriminative in this regard. Studying functional connectivity using Granger Causality allowed us to identify the changes in connectivity detected during the presence of a target stimulus compared to a non-target stimulus. This additional information about the connectivity differences found can be incorporated as a new feature in further studies, allowing for better detection of the P300 signal and consequently improving the performance of P300-based BCIs.

Original language	English
Title of host publication	Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings
Editors	Ignacio Rojas, Gonzalo Joya, Andreu Catala
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	253-264
Number of pages	12
ISBN (Print)	9783030850296
DOIs	https://doi.org/10.1007/978-3-030-85030-2_21
State	Published - 2021
Event	16th International Work-Conference on Artificial Neural Networks, IWANN 2021 - Virtual, Online Duration: 16 Jun 2021 → 18 Jun 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12861 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Work-Conference on Artificial Neural Networks, IWANN 2021
City	Virtual, Online
Period	16/06/21 → 18/06/21

Bibliographical note

Funding Information:
This work was funded by Spanish projects of Ministerio de Econom?a y Competitividad/FEDER TIN2017-84452-R, PID2020-114867RB-I00 (http://www.mineco.gob.es/), postgraduate research grants CZ03-000292-2018, 2015-AR2Q9086 of the Government of Ecuador through SENESCYT and Universidad Polit?cnica Salesiana 041-02-2021-04-16.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Bayesian linear discriminant analysis
Brain networks
EEG signal
Event-related potential
Functional connectivity
Inter-subject variability
Oddball paradigm
Sequential forward electrode selection
Standard electrodes

Access to Document

10.1007/978-3-030-85030-2_21

Cite this

Salazar, V., Changoluisa, V., & Rodriguez, F. B. (2021). P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies. In I. Rojas, G. Joya, & A. Catala (Eds.), Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings (pp. 253-264). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12861 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85030-2_21

Salazar, Vanessa ; Changoluisa, Vinicio ; Rodriguez, Francisco B. / P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies. Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. editor / Ignacio Rojas ; Gonzalo Joya ; Andreu Catala. Springer Science and Business Media Deutschland GmbH, 2021. pp. 253-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The analysis of connectivity in brain networks has been widely researched and it has been shown that certain cognitive processes require the integration of distributed brain areas. Functional connectivity attempts to statistically quantify the interdependence between these brain areas. In this paper, we propose an analysis of functional connectivity in the Event-Related Potential (ERP) context, more specifically on the P300 component using the Granger Causality measure. To this end, we propose a methodology that consists in quantifying the causality in the P300 and non-P300 signals in the context of Brain-Computer Interfaces (BCIs). Causality is calculated using two approaches: i) using standard electrodes and, ii) using electrodes selected using Bayesian Linear Discriminant Analysis and sequential forward electrode selection (BLDA-FS). Based on this analysis, it is shown that the Granger Causality metric is valid to show a significant connectivity difference between P300 and non-P300 signals. The electrodes selected using BLDA-FS were found to be more discriminative in this regard. Studying functional connectivity using Granger Causality allowed us to identify the changes in connectivity detected during the presence of a target stimulus compared to a non-target stimulus. This additional information about the connectivity differences found can be incorporated as a new feature in further studies, allowing for better detection of the P300 signal and consequently improving the performance of P300-based BCIs.",

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Salazar, V, Changoluisa, V & Rodriguez, FB 2021, P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies. in I Rojas, G Joya & A Catala (eds), Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12861 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 253-264, 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual, Online, 16/06/21. https://doi.org/10.1007/978-3-030-85030-2_21

P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies. / Salazar, Vanessa; Changoluisa, Vinicio; Rodriguez, Francisco B.
Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. ed. / Ignacio Rojas; Gonzalo Joya; Andreu Catala. Springer Science and Business Media Deutschland GmbH, 2021. p. 253-264 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12861 LNCS).

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

TY - GEN

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AU - Changoluisa, Vinicio

AU - Rodriguez, Francisco B.

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N2 - The analysis of connectivity in brain networks has been widely researched and it has been shown that certain cognitive processes require the integration of distributed brain areas. Functional connectivity attempts to statistically quantify the interdependence between these brain areas. In this paper, we propose an analysis of functional connectivity in the Event-Related Potential (ERP) context, more specifically on the P300 component using the Granger Causality measure. To this end, we propose a methodology that consists in quantifying the causality in the P300 and non-P300 signals in the context of Brain-Computer Interfaces (BCIs). Causality is calculated using two approaches: i) using standard electrodes and, ii) using electrodes selected using Bayesian Linear Discriminant Analysis and sequential forward electrode selection (BLDA-FS). Based on this analysis, it is shown that the Granger Causality metric is valid to show a significant connectivity difference between P300 and non-P300 signals. The electrodes selected using BLDA-FS were found to be more discriminative in this regard. Studying functional connectivity using Granger Causality allowed us to identify the changes in connectivity detected during the presence of a target stimulus compared to a non-target stimulus. This additional information about the connectivity differences found can be incorporated as a new feature in further studies, allowing for better detection of the P300 signal and consequently improving the performance of P300-based BCIs.

AB - The analysis of connectivity in brain networks has been widely researched and it has been shown that certain cognitive processes require the integration of distributed brain areas. Functional connectivity attempts to statistically quantify the interdependence between these brain areas. In this paper, we propose an analysis of functional connectivity in the Event-Related Potential (ERP) context, more specifically on the P300 component using the Granger Causality measure. To this end, we propose a methodology that consists in quantifying the causality in the P300 and non-P300 signals in the context of Brain-Computer Interfaces (BCIs). Causality is calculated using two approaches: i) using standard electrodes and, ii) using electrodes selected using Bayesian Linear Discriminant Analysis and sequential forward electrode selection (BLDA-FS). Based on this analysis, it is shown that the Granger Causality metric is valid to show a significant connectivity difference between P300 and non-P300 signals. The electrodes selected using BLDA-FS were found to be more discriminative in this regard. Studying functional connectivity using Granger Causality allowed us to identify the changes in connectivity detected during the presence of a target stimulus compared to a non-target stimulus. This additional information about the connectivity differences found can be incorporated as a new feature in further studies, allowing for better detection of the P300 signal and consequently improving the performance of P300-based BCIs.

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KW - Oddball paradigm

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KW - Standard electrodes

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Salazar V, Changoluisa V, Rodriguez FB. P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies. In Rojas I, Joya G, Catala A, editors, Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 253-264. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-85030-2_21

P300 Characterization Through Granger Causal Connectivity in the Context of Brain-Computer Interface Technologies

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Keywords

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