Literature DB >> 22156069

Context-aware brain-computer interfaces: exploring the information space of user, technical system and environment.

T O Zander1, S Jatzev.   

Abstract

Brain-computer interface (BCI) systems are usually applied in highly controlled environments such as research laboratories or clinical setups. However, many BCI-based applications are implemented in more complex environments. For example, patients might want to use a BCI system at home, and users without disabilities could benefit from BCI systems in special working environments. In these contexts, it might be more difficult to reliably infer information about brain activity, because many intervening factors add up and disturb the BCI feature space. One solution for this problem would be adding context awareness to the system. We propose to augment the available information space with additional channels carrying information about the user state, the environment and the technical system. In particular, passive BCI systems seem to be capable of adding highly relevant context information-otherwise covert aspects of user state. In this paper, we present a theoretical framework based on general human-machine system research for adding context awareness to a BCI system. Building on that, we present results from a study on a passive BCI, which allows access to the covert aspect of user state related to the perceived loss of control. This study is a proof of concept and demonstrates that context awareness could beneficially be implemented in and combined with a BCI system or a general human-machine system. The EEG data from this experiment are available for public download at www.phypa.org.

Entities:  

Mesh:

Year:  2011        PMID: 22156069     DOI: 10.1088/1741-2560/9/1/016003

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  15 in total

1.  Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity.

Authors:  Thorsten O Zander; Laurens R Krol; Niels P Birbaumer; Klaus Gramann
Journal:  Proc Natl Acad Sci U S A       Date:  2016-12-12       Impact factor: 11.205

2.  Reducing power line noise in EEG and MEG data via spectrum interpolation.

Authors:  Sabine Leske; Sarang S Dalal
Journal:  Neuroimage       Date:  2019-01-11       Impact factor: 6.556

3.  Editorial: Using neurophysiological signals that reflect cognitive or affective state.

Authors:  Jan B F van Erp; Anne-Marie Brouwer; Thorsten O Zander
Journal:  Front Neurosci       Date:  2015-05-29       Impact factor: 4.677

4.  Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach.

Authors:  Peter Gerjets; Carina Walter; Wolfgang Rosenstiel; Martin Bogdan; Thorsten O Zander
Journal:  Front Neurosci       Date:  2014-12-09       Impact factor: 4.677

5.  Developing an EEG-based on-line closed-loop lapse detection and mitigation system.

Authors:  Yu-Te Wang; Kuan-Chih Huang; Chun-Shu Wei; Teng-Yi Huang; Li-Wei Ko; Chin-Teng Lin; Chung-Kuan Cheng; Tzyy-Ping Jung
Journal:  Front Neurosci       Date:  2014-10-13       Impact factor: 4.677

6.  Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment.

Authors:  Pietro Aricò; Gianluca Borghini; Gianluca Di Flumeri; Alfredo Colosimo; Stefano Bonelli; Alessia Golfetti; Simone Pozzi; Jean-Paul Imbert; Géraud Granger; Raïlane Benhacene; Fabio Babiloni
Journal:  Front Hum Neurosci       Date:  2016-10-26       Impact factor: 3.169

7.  EEG-based workload estimation across affective contexts.

Authors:  Christian Mühl; Camille Jeunet; Fabien Lotte
Journal:  Front Neurosci       Date:  2014-06-12       Impact factor: 4.677

Review 8.  Errare machinale est: the use of error-related potentials in brain-machine interfaces.

Authors:  Ricardo Chavarriaga; Aleksander Sobolewski; José Del R Millán
Journal:  Front Neurosci       Date:  2014-07-22       Impact factor: 4.677

9.  Estimating endogenous changes in task performance from EEG.

Authors:  Jon Touryan; Gregory Apker; Brent J Lance; Scott E Kerick; Anthony J Ries; Kaleb McDowell
Journal:  Front Neurosci       Date:  2014-06-13       Impact factor: 4.677

10.  Affective Aspects of Perceived Loss of Control and Potential Implications for Brain-Computer Interfaces.

Authors:  Sebastian Grissmann; Thorsten O Zander; Josef Faller; Jonas Brönstrup; Augustin Kelava; Klaus Gramann; Peter Gerjets
Journal:  Front Hum Neurosci       Date:  2017-07-19       Impact factor: 3.169

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