title: Perception and Cognition of Cues Used in Synchronous Brain-Computer Interfaces Modify Electroencephalographic Patterns of Control Tasks.
abstract: A motor imagery (MI)-based brain-computer interface (BCI) is a system that enables humans to interact with their environment by translating their brain signals into control commands for a target device. In particular, synchronous BCI systems make use of cues to trigger the motor activity of interest. So far, it has been shown that electroencephalographic (EEG) patterns before and after cue onset can reveal the user cognitive state and enhance the discrimination of MI-related control tasks. However, there has been no detailed investigation of the nature of those EEG patterns. We, therefore, propose to study the cue effects on MI-related control tasks by selecting EEG patterns that best discriminate such control tasks, and analyzing where those patterns are coming from. The study was carried out using two methods: standard and all-embracing. The standard method was based on sources (recording sites, frequency bands, and time windows), where the modulation of EEG signals due to motor activity is typically detected. The all-embracing method included a wider variety of sources, where not only motor activity is reflected. The findings of this study showed that the classification accuracy (CA) of MI-related control tasks did not depend on the type of cue in use. However, EEG patterns that best differentiated those control tasks emerged from sources well defined by the perception and cognition of the cue in use. An implication of this study is the possibility of obtaining different control commands that could be detected with the same accuracy. Since different cues trigger control tasks that yield similar CAs, and those control tasks produce EEG patterns differentiated by the cue nature, this leads to accelerate the brain-computer communication by having a wider variety of detectable control commands. This is an important issue for Neuroergonomics research because neural activity could not only be used to monitor the human mental state as is typically done, but this activity might be also employed to control the system of interest.
authors.affiliation: Brain-Computer Interfaces (BCI) Group, School of Computing Science and Electronic Engineering, University of Essex , Colchester , UK ; Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey - Campus Ciudad de México , Mexico City , Mexico.
authors.forename: Luz María
authors.initials: LM
authors.lastname: Alonso-Valerdi
authors.affiliation: Brain-Computer Interfaces (BCI) Group, School of Computing Science and Electronic Engineering, University of Essex , Colchester , UK.
authors.forename: Francisco
authors.initials: F
authors.lastname: Sepulveda
authors.affiliation: Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey - Campus Ciudad de México , Mexico City , Mexico.
authors.forename: Ricardo A
authors.initials: RA
authors.lastname: Ramírez-Mendoza
keywords: brain–computer interface; classification accuracy; electroencephalographic patterns; human factors; motor imagery