A hierarchical architecture for recognising intentionality in mental tasks on a brain-computer interface

Asier Salazar-Ramirez, Jose I Martin, Raquel Martinez, Andoni Arruti, Javier Muguerza and Basilio Sierra

PLOS ONE, 2019, vol. 14, issue 6, 1-18

Abstract: A brain-computer interface (BCI), based on motor imagery EEG, uses information extracted from the electroencephalography signals generated by a person who intends to perform any action. One of the most important issues of current research is how to detect automatically whether the user intends to send some message to a certain device. This study presents a proposal, based on a hierarchical structured system, for recognising intentional and non-intentional mental tasks on a BCI system by applying machine learning techniques to the EEG signals. First-level clustering is performed to distinguish between intentional control (IC) and non-intentional control (NC) state patterns. Then, the patterns recognised as IC are passed on to a second stage where supervised learning techniques are used to classify them. In BCI applications, it is critical to correctly classify NC states with a low false positive rate (FPR) to avoid undesirable effects. According to the literature, we selected a maximum FPR of 10%. Under these conditions, our proposal achieved an average test accuracy of 66.6%, with an 8.2% FPR, for the BCI competition IIIa dataset. The main contribution of this paper is the hierarchical approach, based on machine learning paradigms, which performs intentional and non-intentional discrimination and, depending on the case, classifies the intended command selected by the user.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0218181

DOI: 10.1371/journal.pone.0218181

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