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

Vanessa Salazar, Vinicio Changoluisa, Francisco B. Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationAdvances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings
EditorsIgnacio Rojas, Gonzalo Joya, Andreu Catala
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030850296
StatePublished - 2021
Event16th International Work-Conference on Artificial Neural Networks, IWANN 2021 - Virtual, Online
Duration: 16 Jun 202118 Jun 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12861 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Work-Conference on Artificial Neural Networks, IWANN 2021
CityVirtual, Online

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.


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


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