research-article

Towards cooperative brain-computer interfaces for space navigation

Authors:

Caterina Cinel,

Ana Matran-Fernandez,

Francisco Sepulveda,

Adrian StoicaAuthors Info & Claims

IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

Pages 149 - 160

https://doi.org/10.1145/2449396.2449417

Published: 19 March 2013 Publication History

Abstract

We explored the possibility of controlling a spacecraft simulator using an analogue Brain-Computer Interface (BCI) for 2-D pointer control. This is a difficult task, for which no previous attempt has been reported in the literature. Our system relies on an active display which produces event-related potentials (ERPs) in the user's brain. These are analysed in real-time to produce control vectors for the user interface. In tests, users of the simulator were told to pass as close as possible to the Sun. Performance was very promising, on average users managing to satisfy the simulation success criterion in 67.5% of the runs. Furthermore, to study the potential of a collaborative approach to spacecraft navigation, we developed BCIs where the system is controlled via the integration of the ERPs of two users. Performance analysis indicates that collaborative BCIs produce trajectories that are statistically significantly superior to those obtained by single users.

References

[1]

Allison, B. Z., Wolpaw, E. W., and Wolpaw, J. R. Brain-computer interface systems: progress and prospects. Expert Review of Medical Devices 4, 4 (Jul 2007), 463--474.

[2]

Bell, C. J., Shenoy, P., Chalodhorn, R., and Rao, R. P. N. Control of a humanoid robot by a noninvasive braincomputer interface in humans. Journal of Neural Engineering 5, 2 (2008), 214.

[3]

Beverina, F., Palmas, G., Silvoni, S., Piccione, F., and Giove, S. User adaptive BCIs: SSVEP and P300 based interfaces. PsychNology Journal 1, 4 (2003), 331--354.

[4]

Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., and Flor, H. A spelling device for the paralysed. Nature 398, 6725 (Mar 1999), 297--298.

[5]

Birbaumer, N., Hinterberger, T., Kübler, A., and Neumann, N. The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Transactions on Neural System and Rehabilitation Engineering 11, 2 (Jun 2003), 120--123.

[6]

Broschart, M., de Negueruela, C., Millán, J. d. R., and Menon, C. Augmenting astronaut's capabilities through brain-machine interfaces. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, Workshop on Artificial Intelligence for Space Applications (1 2007).

[7]

Citi, L., Poli, R., Cinel, C., and Sepulveda, F. P300-based BCI mouse with genetically-optimized analogue control. IEEE Transactions on Neural Systems and Rehabilitation Engineering 16, 1 (Feb. 2008), 51--61.

[8]

Coffey, E. B., Brouwer, A.-M., Wilschut, E. S., and van Erp, J. B. Brainmachine interfaces in space: Using spontaneous rather than intentionally generated brain signals. Acta Astronautica 67, 1-2 (2010), 1--11.

[9]

Coyle, S. M., Ward, T. E., and Markham, C. M. Brain-computer interface using a simplified functional near-infrared spectroscopy system. Journal of Neural Engineering 4, 3 (Sep 2007), 219--226.

[10]

Curran, E. A., and Stokes, M. J. Learning to control brain activity: a review of the production and control of EEG components for driving brain-computer interface (BCI) systems. Brain Cognition 51, 3 (Apr 2003), 326--336.

[11]

Donoghue, J. Connecting cortex to machines: recent advances in brain interfaces. Nature Neuroscience 5 (2002), 1085--1088.

[12]

Eckstein, M. P., Das, K., Pham, B. T., Peterson, M. F., Abbey, C. K., Sy, J. L., and Giesbrecht, B. Neural decoding of collective wisdom with multi-brain computing. NeuroImage 59, 1 (2012), 94--108.

[13]

Farwell, L. A., and Donchin, E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalography and Clinical Neurophysiology 70, 6 (Dec 1988), 510--523.

[14]

Finke, A., Knoblauch, A., Koesling, H., and Ritter, H. J. A hybrid brain interface for a humanoid robot assistant. In 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2011), IEEE, IEEE (Boston, MA, USA, 30.08.2011 2011).

[15]

Galán, F., Nuttin, M., Lew, E., Ferrez, P., Vanacker, G., Philips, J., and Millán, J. A brain-actuated wheelchair: Asynchronous and non-invasive brain-computer interfaces for continuous control of robots. Clinical Neurophysiololy (2008).

[16]

Georgopoulos, A., Langheim, F., Leuthold, A., and Merkle, A. Magnetoencephalographic signals predict movement trajectory in space. Experimental Brain Research (Jul 2005), 1--4.

[17]

Hand, D. J., and Till, R. J. A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning 45 (2001), 171--186.

Digital Library

[18]

Hilton, B. D. Space commander (version 0.4). http://spacecommander.sourceforge.net/, 2006.

[19]

Lebedev, M., and Nicolelis, M. Brain-machine interfaces: past, present and future. Trends in Neurosciences 29, 9 (September 2006), 536--546.

[20]

Luck, S. J. An introduction to the event-related potential technique. MIT Press, Cambridge, Massachusetts, 2005.

[21]

Menon, C., de Negueruela, C., del Millán, J., Tonet, O., Carpi, F., Broschart, M., Ferrez, P., Buttfield, A., Dario, P., Citi, L., Laschi, C., Tombini, M., Sepulveda, F., Poli, R., Palaniappan, R., Tecchio, F., Rossini, P. M., and Rossi, D. D. Prospective on brain-machine interfaces for space system control. In 57th Astronautical Congress (Spain, 2-6 Oct 2006). paper IAC-06-D1.1.05.

[22]

Menon, C., de Negueruela, C., del Millán, J., Tonet, O., Carpi, F., Broschart, M., Ferrez, P., Buttfield, A., Tecchio, F., Sepulveda, F., Citi, L., Laschi, C., Tombini, M., Dario, P., Rossini, M., and Rossi, D. D. Prospects of brain-machine interfaces for space system control. Acta Astronautica 64, 4 (2009), 448--456.

[23]

Millan, J. D. R., Renkens, F., Mourino, J., and Gerstner, W. Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Transactions on Biomedical Engineering 51 (2004), 1026--1033.

[24]

Müller, G. R., Scherer, R., Neuper, C., and Pfurtscheller, G. Steady-state somatosensori evoked potentials: Suitable brain signals for brain-computer interfaces? IEEE Transactions on Neural Systems and Rehabilitation Engineering 14, 1 (2006), 30--37.

[25]

Pfurtscheller, G., Flotzinger, D., and Kalcher, J. Brain-computer interface: a new communication device for handicapped persons. Journal of Microcomputer Applications 16, 3 (1993), 293--299.

Digital Library

[26]

Poli, R., Cinel, C., Sepulveda, F., and Stoica, A. Improving decision-making based on visual perception via a collaborative brain-computer interface. In IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), IEEE (San Diego (CA), February 2013). (to appear).

[27]

Poli, R., Salvaris, M., and Cinel, C. Evolutionary synthesis of a trajectory integrator for an analogue brain-computer interface mouse. In Applications of Evolutionary Computing, EvoApplications 2011, C. Di Chio, S. Cagnoni, C. Cotta, M. Ebner, A. Ekart, A. I. Esparcia-Alcazar, J. J. Merelo, F. Neri, M. Preuss, H. Richter, J. Togelius, and G. N. Yannakakis, Eds., vol. 6624 of LNCS, Springer Verlag (Turin, Italy, 27-29 Apr. 2011), 214--223.

Digital Library

[28]

Polikoff, J. B., Bunnell, H. T., and Borkowski Jr, W. J. Toward a P300-based computer interface. In Rehabilitation Engineering and Assistive Technology Society of North America (RESNA'95), Resna Press (Arlington, Va, 1995), 178--180.

[29]

Reza, F.-R., Z, A. B., Christoph, G., W, S. E., C, K. S., and Andrea, K. P300 brain computer interface: current challenges and emerging trends. Frontiers in Neuroengineering 5, 14 (2012).

[30]

Salvaris, M., Cinel, C., Citi, L., and Poli, R. Novel protocols for P300-based brain-computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 1 (2012), 8--17.

[31]

Schwartz, A. B. Cortical neural prosthetics. Annual Review of Neuroscience 27 (2004), 487--507.

[32]

Sellers, E. W., and Donchin, E. A P300-based brain-computer interface: Initial tests by ALS patients. Clinical Neurophysiology 117, 3 (Mar 2006), 538--548.

[33]

Soon, C. S., Brass, M., Heinze, H.-J., and Haynes, J.-D. Unconscious determinants of free decisions in the human brain. Nature Neuroscience 11, 5 (April 2008), 543--545.

[34]

Stoica, A. Multimind: Multi-brain signal fusion to exceed the power of a single brain. In EST, A. Stoica, D. Zarzhitsky, G. Howells, C. D. Frowd, K. D. McDonald-Maier, A. T. Erdogan, and T. Arslan, Eds., IEEE Computer Society (2012), 94--98.

Digital Library

[35]

Trejo, L. J., Rosipal, R., and Matthews, B. Brain-computer interfaces for 1-D and 2-D cursor control: Designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14, 2 (June 2006), 225--229.

[36]

Tzovara, A., Murray, M. M., Bourdaud, N., Chavarriaga, R., del R. Millán, J., Lucia, M. D., and Lucia, M. D. The timing of exploratory decision-making revealed by single-trial topographic EEG analyses. NeuroImage (2012), 1959--1969.

[37]

Wang, Y., and Jung, T.-P. A Collaborative Brain-Computer Interface for Improving Human Performance. PLoS ONE 6, 5 (May 2011), e20422+.

[38]

Weiskopf, N., Mathiak, K., Bock, S. W., Scharnowski, F., Veit, R., Grodd, W., Goebel, R., and Birbaumer, N. Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI). IEEE Transactions on Biomedical Engineering 51, 6 (Jun 2004), 966--970.

[39]

Wilson, J. J., and Palaniappan, R. Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface. Journal of Neural Engineering 8 (October 2011).

[40]

Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., and Vaughan, T. M. Brain-computer interfaces for communication and control. Clinical Neurophysiology 113, 6 (June 2002), 767--91.

[41]

Wolpaw, J. R., and McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proceedings of the National Academy of Sciences 101, 51 (2004), 17849--17854.

[42]

Wolpaw, J. R., McFarland, D. J., Neat, G. W., and Forneris, C. A. An EEG-based brain-computer interface for cursor control. Electroencephalography and Clinical Neurophysiology 78, 3 (Mar 1991), 252--259.

[43]

Yuan, P., Wang, Y., Wu, W., Xu, H., Gao, X., and Gao, S. Study on an online collaborative BCI to accelerate response to visual targets. In Proceedings of 34nd IEEE EMBS Conference (2012).

Cited By

Song XZeng YTong LShu JYang QKou JSun MYan B(2022)A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual TargetsComputational Intelligence and Neuroscience10.1155/2022/47524502022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4752450
Putze FPutze SSagehorn MMicek CSolovey E(2022)Understanding HCI Practices and Challenges of Experiment Reporting with Brain Signals: Towards Reproducibility and ReuseACM Transactions on Computer-Human Interaction10.1145/349055429:4(1-43)Online publication date: 31-Mar-2022
https://dl.acm.org/doi/10.1145/3490554
Bernal SCeldrán APérez GBarros MBalasubramaniam S(2021)Security in Brain-Computer InterfacesACM Computing Surveys10.1145/342737654:1(1-35)Online publication date: 2-Jan-2021
https://dl.acm.org/doi/10.1145/3427376
Show More Cited By

Index Terms

Towards cooperative brain-computer interfaces for space navigation
1. Human-centered computing

Recommendations

Towards ambulatory brain-computer interfaces: a pilot study with P300 signals
ACE '09: Proceedings of the International Conference on Advances in Computer Entertainment Technology

Brain-Computer Interfaces (BCI) are communication systems that enable users to interact with computers using only brain activity. This activity is generally measured by ElectroEncephaloGraphy (EEG). A major limitation of BCI is the electrical ...
Brain-Computer Interfaces: Current Trends and Applications
Robot Navigation Using Brain-Computer Interfaces
TRUSTCOM '12: Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications

This paper identifies the user's adaptation on brain-controlled systems and the ability to control brain-generated events in a closed neuro-feedback loop. To accomplish that, a working system has been developed based on off-the-shelf components for ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '13: Proceedings of the 2013 international conference on Intelligent user interfaces

March 2013

470 pages

ISBN:9781450319652

DOI:10.1145/2449396

General Chair:
Jihie Kim
University of Southern California, USA
,
Program Chairs:
Jeffrey Nichols
IBM Research -- Almaden, USA
,
Pedro Szekely
University of Southern California, USA

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 March 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IUI '13

Sponsor:

IUI '13: 18th International Conference on Intelligent User Interfaces

March 19 - 22, 2013

California, Santa Monica, USA

Acceptance Rates

IUI '13 Paper Acceptance Rate 43 of 192 submissions, 22%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

54
Total Citations
View Citations
654
Total Downloads

Downloads (Last 12 months)21
Downloads (Last 6 weeks)2

Reflects downloads up to 16 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Song XZeng YTong LShu JYang QKou JSun MYan B(2022)A Collaborative Brain-Computer Interface Framework for Enhancing Group Detection Performance of Dynamic Visual TargetsComputational Intelligence and Neuroscience10.1155/2022/47524502022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4752450
Putze FPutze SSagehorn MMicek CSolovey E(2022)Understanding HCI Practices and Challenges of Experiment Reporting with Brain Signals: Towards Reproducibility and ReuseACM Transactions on Computer-Human Interaction10.1145/349055429:4(1-43)Online publication date: 31-Mar-2022
https://dl.acm.org/doi/10.1145/3490554
Bernal SCeldrán APérez GBarros MBalasubramaniam S(2021)Security in Brain-Computer InterfacesACM Computing Surveys10.1145/342737654:1(1-35)Online publication date: 2-Jan-2021
https://dl.acm.org/doi/10.1145/3427376
Bhattacharyya SValeriani DCinel CCiti LPoli R(2021)Anytime collaborative brain–computer interfaces for enhancing perceptual group decision-makingScientific Reports10.1038/s41598-021-96434-011:1Online publication date: 20-Aug-2021
https://doi.org/10.1038/s41598-021-96434-0
Angelica AOpris ILebedev MBoehm F(2021)Cognitive Augmentation Via a Brain/Cloud InterfaceModern Approaches to Augmentation of Brain Function10.1007/978-3-030-54564-2_17(357-386)Online publication date: 26-Aug-2021
https://doi.org/10.1007/978-3-030-54564-2_17
Lebedev MShaderkin IRyabkov ILebedev G(2021)Augmentation Through Interconnection: Brain-Nets and TelemedicineModern Approaches to Augmentation of Brain Function10.1007/978-3-030-54564-2_16(343-355)Online publication date: 26-Aug-2021
https://doi.org/10.1007/978-3-030-54564-2_16
Martins NAngelica AChakravarthy KSvidinenko YBoehm FOpris ILebedev MSwan MGaran SRosenfeld JHogg TFreitas R(2019)Human Brain/Cloud InterfaceFrontiers in Neuroscience10.3389/fnins.2019.0011213Online publication date: 29-Mar-2019
https://doi.org/10.3389/fnins.2019.00112
Cinel CValeriani DPoli R(2019)Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future ProspectsFrontiers in Human Neuroscience10.3389/fnhum.2019.0001313Online publication date: 31-Jan-2019
https://doi.org/10.3389/fnhum.2019.00013
Zhang JSu CZapala DZhu LCui GKong W(2019)A CNN-based Approach for three-class classification of motor imagery EEG data including ‘rest state’ in hybrid multi-user BCI2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM47256.2019.8983380(770-773)Online publication date: Nov-2019
https://doi.org/10.1109/BIBM47256.2019.8983380
Matran-Fernandez APoli R(2017)Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfacesPLOS ONE10.1371/journal.pone.017849812:5(e0178498)Online publication date: 31-May-2017
https://doi.org/10.1371/journal.pone.0178498
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten