Citation

Grissmann S, Zander TO, Faller J, Brönstrup J, Kelava A, Gramann K, Gerjets P. Front. Hum. Neurosci. 2017; 11: e370.

Affiliation

Leibniz-Institut für Wissensmedien, University of TübingenTübingen, Germany.

Copyright

(Copyright © 2017, Frontiers Research Foundation)

DOI

10.3389/fnhum.2017.00370

PMID

28769776

PMCID

PMC5515824

Abstract

Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.

Language: en

Keywords

brain-computer interface (BCI); electroencephalography (EEG); frontal alpha asymmetry (FAA); independent component analysis (ICA); loss of control (LOC)