poster

EasyEG: A 3D-printable Brain-Computer Interface

Authors:

Stefan SchneegassAuthors Info & Claims

UIST '20 Adjunct: Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

Pages 70 - 72

https://doi.org/10.1145/3379350.3416189

Published: 20 October 2020 Publication History

Get Access

Abstract

Brain-Computer Interfaces (BCIs) are progressively adopted by the consumer market, making them available for a variety of use-cases. However, off-the-shelf BCIs are limited in their adjustments towards individual head shapes, evaluation of scalp-electrode contact, and extension through additional sensors. This work presents EasyEG, a BCI headset that is adaptable to individual head shapes and offers adjustable electrode-scalp contact to improve measuring quality. EasyEG consists of 3D-printed and low-cost components that can be extended by additional sensing hardware, hence expanding the application domain of current BCIs. We conclude with use-cases that demonstrate the potentials of our EasyEG headset.

References

[1]

Aadeel Akhtar, James Norton, Mahsa Kasraie, and Timothy Bretl. 2014. Playing checkers with your mind: An interactive multiplayer hardware game platform for brain-computer interfaces. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1650--1653.

Crossref

Google Scholar

[2]

Audrey Aldridge, Eli Barnes, Cindy L. Bethel, Daniel W. Carruth, Marianna Kocturova, Matus Pleva, and Jozef Juhar. 2019. Accessible Electroencephalograms (EEGs): A Comparative Review with OpenBCI's Ultracortex Mark IV Headset. In 2019 29th International Conference Radioelektronika (RADIOELEKTRONIKA). 1--6.

Crossref

Google Scholar

[3]

Andrew Campbell, Tanzeem Choudhury, Shaohan Hu, Hong Lu, Matthew K. Mukerjee, Mashfiqui Rabbi, and Rajeev D.S. Raizada. 2010. NeuroPhone: Brain-Mobile Phone Interface Using a Wireless EEG Headset. In Proceedings of the Second ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds (MobiHeld '10). Association for Computing Machinery, New York, NY, USA, 3--8.

Digital Library

Google Scholar

[4]

Jérémy Frey, Maxime Daniel, Julien Castet, Martin Hachet, and Fabien Lotte. 2016. Framework for Electroencephalography-Based Evaluation of User Experience. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, NY, USA, 2283--2294.

Digital Library

Google Scholar

[5]

Alan Gevins, Michael E. Smith, Linda McEvoy, and Dou Yu. 1997. High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cerebral Cortex 7, 4 (06 1997), 374--385.

Crossref

Google Scholar

[6]

Mariam Hassib, Stefan Schneegass, Philipp Eiglsperger, Niels Henze, Albrecht Schmidt, and Florian Alt. 2017. EngageMeter: A System for Implicit Audience Engagement Sensing Using Electroencephalography. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). Association for Computing Machinery, New York, NY, USA, 5114--5119.

Digital Library

Google Scholar

[7]

Wolfgang Klimesch, Michael Doppelmayr, Harald Russegger, Thomas Pachinger, and Jens Schwaiger. 1998. Induced alpha band power changes in the human EEG and attention. Neuroscience Letters 244, 2 (1998), 73 -- 76.

Crossref

Google Scholar

[8]

Thomas Kosch, Markus Funk, Albrecht Schmidt, and Lewis L. Chuang. 2018. Identifying Cognitive Assistance with Mobile Electroencephalography: A Case Study with In-Situ Projections for Manual Assembly. Proc. ACM Hum.-Comput. Interact. 2, EICS, Article 11 (June 2018), 20 pages.

Digital Library

Google Scholar

[9]

Thomas Kosch, Mariam Hassib, and Albrecht Schmidt. 2016. The Brain Matters: A 3D Real-Time Visualization to Examine Brain Source Activation Leveraging Neurofeedback. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '16). ACM, New York, NY, USA, 1570--1576.

Digital Library

Google Scholar

[10]

Fabien Lotte, Laurent Bougrain, Andrzej Cichocki, Maureen Clerc, Marco Congedo, Alain Rakotomamonjy, and Florian Yger. 2018. A review of classification algorithms for EEG-based brain--computer interfaces: a 10 year update. Journal of Neural Engineering 15, 3 (apr 2018), 031005.

Crossref

Google Scholar

Cited By

View all

Bernardi LHaeublein OZapata D(2024)Design and Development of a 3D Printed Electrode Headset for Affordable and Reliable ElectroencephalographyAdvances in Bioengineering and Clinical Engineering10.1007/978-3-031-61973-1_17(159-168)Online publication date: 8-Jun-2024
https://doi.org/10.1007/978-3-031-61973-1_17
Auda JGruenefeld UKosch TSchneegass S(2022)The Butterfly Effect: Novel Opportunities for Steady-State Visually-Evoked Potential Stimuli in Virtual RealityProceedings of the Augmented Humans International Conference 202210.1145/3519391.3519397(254-266)Online publication date: 13-Mar-2022
https://dl.acm.org/doi/10.1145/3519391.3519397

Index Terms

EasyEG: A 3D-printable Brain-Computer Interface
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile devices

Recommendations

Brain-Computer Interface using Directional Auditory Perception
AHs '23: Proceedings of the Augmented Humans International Conference 2023

We investigate the potential of brain-computer interface (BCI) using electroencephalogram (EEG) induced by listening (or recalling) auditory stimuli of different directions. In the initial attempt, we apply a time series classification model based on ...
Brain-computer interface research at Katholieke Universiteit Leuven
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies

We present an overview of our Brain-computer interface (BCI) research, invasive as well as non-invasive, during the past four years. The invasive BCIs are based on local field-and action potentials recorded with microelectrode arrays implanted in the ...
Brain--computer interface (BCI): is it strictly necessary to use random sequences in visual spellers?
APCHI '12: Proceedings of the 10th asia pacific conference on Computer human interaction

The P300 speller is a standard paradigm for brain--computer interfacing (BCI) based on electroencephalography (EEG). It exploits the fact that the user's selective attention to a target stimulus among a random sequence of stimuli enhances the magnitude ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

UIST '20 Adjunct: Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

October 2020

203 pages

ISBN:9781450375153

DOI:10.1145/3379350

General Chairs:
Shamsi Iqbal
Microsoft Research, USA
,
Karon MacLean
University of British Columbia, Canada
,
Program Chairs:
Fanny Chevalier
University of Toronto, Canada
,
Stefanie Mueller
MIT CSAIL, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 October 2020

Check for updates

Author Tags

Qualifiers

Poster

Conference

UIST '20

Sponsor:

UIST '20: The 33rd Annual ACM Symposium on User Interface Software and Technology

October 20 - 23, 2020

Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 561 of 2,567 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
188
Total Downloads

Downloads (Last 12 months)20
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Bernardi LHaeublein OZapata D(2024)Design and Development of a 3D Printed Electrode Headset for Affordable and Reliable ElectroencephalographyAdvances in Bioengineering and Clinical Engineering10.1007/978-3-031-61973-1_17(159-168)Online publication date: 8-Jun-2024
https://doi.org/10.1007/978-3-031-61973-1_17
Auda JGruenefeld UKosch TSchneegass S(2022)The Butterfly Effect: Novel Opportunities for Steady-State Visually-Evoked Potential Stimuli in Virtual RealityProceedings of the Augmented Humans International Conference 202210.1145/3519391.3519397(254-266)Online publication date: 13-Mar-2022
https://dl.acm.org/doi/10.1145/3519391.3519397

View Options

Login options

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

Cited By

Index Terms

Recommendations

Brain-Computer Interface using Directional Auditory Perception

Brain-computer interface research at Katholieke Universiteit Leuven

Brain--computer interface (BCI): is it strictly necessary to use random sequences in visual spellers?