Implementing a fuzzy inference system in a multi-objective EEG channel selection model for imagined speech classification

• Published in: Expert systems with applications 2016-10, Vol.59, p.1-12
• Main Authors: Torres-García, Alejandro A., Reyes-García, Carlos A., Villaseñor-Pineda, Luis, García-Aguilar, Gregorio
• Format: Article
• Language: English
• Subjects: Accuracy; Brain-computer interfaces (BCI); Channel selection; Channels; Classification; Electroencephalograms (EEG); Electroencephalography; Fuzzy; Fuzzy inference system (FIS); Human-computer interface; Imagined speech; Mathematical models; Speech
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2016.04.011

•It was searched the minimal subset of channels for imagined speech.•Channel selection was approached as multi-objective to obtain a Pareto front.•A fuzzy system inference was applied to find a promising solution from Pareto front.•Channel selection had a statistically similar performance to the use of all channels.•It was observed a dependence between features and classes of imagined speech. One of the main purposes of brain-computer interfaces (BCI) is to provide persons of an alternative communication channel. This objective was firstly focused on handicapped subjects but nowadays its scope has increased to healthy persons. Usually, BCIs record brain activity using electroencephalograms (EEG), according to four main neuro-paradigms (slow cortical potentials, motor imagery, P300 component and visual evoked potentials). These analytical paradigms are not intuitive and are difficult to implement. Accordingly, this work researches an alternative neuro-paradigm called imagined speech, which refers to the internal pronunciation of words without emitting sounds or doing facial movements. Specifically, the present research is focused on the recognition of five Spanish words corresponding to the English words “up,” “down,” “left,” “right” and “select”, with which a computer cursor could be controlled. We perform an offline computer automatic classification procedure of a dataset of EEG signals from 27 subjects. The method implements a channel selection composed of two stages; the first one obtains a Pareto front and is approached as a multi-objective optimization problem dealing with the error rate and the number of channels; the second stage selects a single solution (channel combination) from the front, applying a fuzzy inference system (FIS). We assess the method’s performance through a channel combination and a test set not used to generate the front. Several FIS configurations were explored to evaluate if a FIS is able to select channel combinations that improve or, at least, keep the obtained accuracies using all channels for each subject’s data. We found that a FIS configuration, FIS3×3 (three membership functions for both input variables: error rate and the number of channels), obtained the best trade-off between the number of fuzzy rules and its accuracy (68.18% using around 7 channels). Also, the FIS3×3 obtained a similar statistically accuracy compared to the use of all channels (70.33%). Results of our method demonstrate the feasibility of using a FI