Biomedical Signal Processing and Control 18 (2015) 11–18 Contents lists available at ScienceDirect Biomedical Signal Processing and Control jo ur nal homepage: www.elsevier.com/locate/bspc An optimized feature selection and classification method for using electroencephalographic coherence in brain–computer interfaces Rocio Salazar-Varas, David Gutiérrez ∗ Center for Research and Advanced Studies (Cinvestav), Monterrey’s Unit, Apodaca, N.L. 66600, Mexico a r t i c l e i n f o Article history: Received 28 August 2014 Received in revised form 18 October 2014 Accepted 6 November 2014 Keywords: Brain–computer interfaces Electroencephalography Coherence Linear discriminant a b s t r a c t We propose a method to use electroencephalographic (EEG) coherences as features in a brain–computer interface (BCI). The coherence provides a sense of the brain’s connectivity, and it is relevant as different regions of the brain must communicate between each other for the integration of sensory information. In our case, the process of feature selection is optimized in the sense that only those statistically significant and potentially discriminative coherences at a specific frequency are used, which results in a feature vec- tor of reduced-dimension. Next, those features are classified through an optimized linear discriminant, where the best discriminating hyperplanes are selected such that the area under the receiver operating characteristics (ROC) curve is maximized. Overall, the proposed EEG coherence selection and classifica- tion method can provide efficiency rates similar to those obtained with other methods in BCI, but with the advantage of blindly selecting and optimal combination of features out of all the possible pairwise coher- ences. We demonstrate the applicability of the proposed method through numerical examples using real data from motor and cognitive tasks. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction A brain–computer interface (BCI) is a communication system that allows a subject to act on his/her environment solely by means of his/her thoughts, i.e. without using the brain’s normal output pathways of muscles or peripheral nerves [1]. Non-invasive BCIs rely on electroencephalographic (EEG) measurements of the brain’s activity to read out the intentions of the subject and translate them into commands for a computerized system. The translation from the brain activity to a command is usually achieved by means of a feature generator that extracts feature values from the EEG signals that correspond to the underlying neurological mechanism employed by the user for control. Next, a feature translator classifies the features into logical control signals, such as a two-state discrete output. Many methods have been proposed so far to carry out the extraction/classification processes in BCI, and a very comprehensive review about them can be found in [2]. In general, feature extraction methods are closely related to ∗ Corresponding author at: Center for Research and Advanced Studies, Mon- terrey’s Unit, Vía del Conocimiento 201, Parque de Investigación e Innovación Tecnológica (PIIT), Autopista al Aeropuerto Km. 9.5, Lote 1, Manzana 29, Apodaca, N.L. 66600, Mexico. Tel.: +52 81 1156 1740x4513; fax: +52 81 1156 1741. E-mail address: dgtz@ieee.org (D. Gutiérrez). specific neuromechanisms, while feature classification algorithms are determined by the type of features that they discriminate. Here, we examine the use of the EEG coherence as feature in a BCI. The coherence provides a sense of the brain’s connectivity, and it is relevant to measure it as different regions widely dis- tributed over the brain must communicate between each other in order to provide the basis for integration of sensory information, as well as for many functions that are critical for learning, mem- ory, information processing, perception, and behavior. Transient periods of synchronization of oscillating neural discharges have been proposed to act as an integrative mechanism that may bring a widely distributed set of neurons together into a coherent ensemble that underlies a cognitive act [3], and many studies have used the EEG coherence to quantify such synchronization process (see [4] and references therein). In [5], the patterns in the coherence were studied during sequential and simultaneous tasks, while in [6], sig- nals corresponding to spontaneous EEG, imaginery movement, and movement execution were classified based on the coherence using hidden Markov models and a multilayer perceptron. Nevertheless, the only attempt known to us of using the coherence in the con- text of BCI can be found in [7]. There, the use of the coherence as a feature was assessed for the case of measuring the mean coupling between signals recorded from an electrode and its neighbors, and a few individual electrode pairs reflecting connectivity between fronto-centro-parietal and temporal lobes. Given the limited num- ber of subjects tested and the coherences that were assessed, their http://dx.doi.org/10.1016/j.bspc.2014.11.001 1746-8094/© 2014 Elsevier Ltd. All rights reserved.