A discriminative multi-output gaussian processes scheme for brain electrical activity analysis

Cristian Torres-Valencia, Álvaro Orozco, David Cárdenas-Peña, Andrés Álvarez-Meza, Mauricio Álvarez

Research output: Contribution to journalArticlepeer-review


The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM) allows us to find and quantify the relationships between different channels. Several MOGPs are trained from different conditions grouped in bi-class problems, and the discrimination is performed based on the likelihood score of the test signals against all the models. Finally, the mean likelihood is computed to predict the correspondence of new inputs with each class’s existing models. Results show that this framework allows us to model the EEG signals adequately using generative models and allows analyzing the relationships between channels of the EEG for a particular condition. At the same time, the set of trained MOGPs is well suited to discriminate new input data.

Original languageEnglish
Article number6765
Pages (from-to)1-15
Number of pages15
JournalApplied Sciences (Switzerland)
Issue number19
Publication statusPublished - 1 Oct 2020


  • Brain electrical activity
  • Electroencephalography
  • Gaussian processes
  • Spectral mixture kernel


Dive into the research topics of 'A discriminative multi-output gaussian processes scheme for brain electrical activity analysis'. Together they form a unique fingerprint.

Cite this