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

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 c

SUBMITTER: Salazar-Ramirez A 

PROVIDER: S-EPMC6581259 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature