Evaluation of Emotiv EEG neuroheadset
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Grzegorz M. Wójcik
,
Piotr Wierzgała
Abstract
Electroencephalography (EEG) has become more popular, and as a result, the market grows with new EEG products. The new EEG solutions offer higher mobility, easier application, and lower price. One of such devices that recently became popular is Emotiv EEG. It has been already tested in various applications concerning brain-computer interfaces, neuromarketing, language processing, and detection of the P-300 component, with a general result that it is capable of recording satisfying research data. However, no one has tested and described its usefulness in long-term research. This article presents experience from using Emotiv EEG in two research projects that involved 39 subjects for 22 sessions. Emotiv EEG has significant technical issues concerning the quality of its screw threads. Two complete and successful solutions to this problem are described.
Acknowledgments
This research has been supported by University of Economics and Innovation in Lublin, Poland (grant no. 12/OP/4/2013/). The authors would like to thank Marcin Smolira, Krzysztof Dmitruk, and Ewa Bogucińska for their support and assistance.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Appendix
Wireframe projections for CAD 3D printout solutions.
References
1. Jasper JH. The ten-twenty electrode system of the international federation. Electroen Clin Neuro 1958;10:371–5.Suche in Google Scholar
2. Bobrov P, Frolov A, Cantor C, Fedulova I, Bakhnyan M, Zhavoronkov A. Brain-computer interface based on generation of visual images. PLoS One 2011;6:1–12.10.1371/journal.pone.0020674Suche in Google Scholar PubMed PubMed Central
3. Liu Y, Jiang X, Cao T, Wan F, Mak PU, Mak PI, et al. Implementation of SSVEP based BCI with Emotiv EPOC. In: Proceedings of the IEEE International Conference on Virtual Environments, Human-Computer Interfaces, and Measurement Systems, VECIMS, July 2–4, 2012. Tianjin, China: IEEE, 2012:34–7.10.1109/VECIMS.2012.6273184Suche in Google Scholar
4. Campbell A, Choudhury T, Hu S, Lu H, Mukerjee MK, Rabbi M, et al. 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, August 30–September 3, 2010. New York: ACM, 2010:3–8.10.1145/1851322.1851326Suche in Google Scholar
5. Shankar SS, Rai R. Human factors study on the usage of BCI headset for 3D CAD modeling. Comput Aided Des 2014;54:51–5.10.1016/j.cad.2014.01.006Suche in Google Scholar
6. Cinar E, Sahin F. New classification techniques for electroencephalogram (EEG) signals and a real-time EEG control of a robot. Neural Comput Appl 2013;22:29–39.10.1007/s00521-011-0744-xSuche in Google Scholar
7. Khushaba RN, Wise C, Kodagoda S, Louviere J, Kahn BE, Townsend C. Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst Appl 2013;40:3803–12.10.1016/j.eswa.2012.12.095Suche in Google Scholar
8. Louwerse M, Hutchinson S. Neurological evidence linguistic processes precede perceptual simulation in conceptual processing. Front Psychol 2012;3:1–11.10.3389/fpsyg.2012.00385Suche in Google Scholar PubMed PubMed Central
9. Duvinage M, Castermans T, Petieau M, Hoellinger T, Cheron G, Dutoit T. Performance of the Emotiv Epoc headset for P300-based applications. BioMed Eng OnLine 2013;12:1–15.10.1186/1475-925X-12-56Suche in Google Scholar PubMed PubMed Central
10. Ramírez-Cortes J, Alarcon-Aquino V, Rosas-Cholula G, Gomez-Gil P, Escamilla-Ambrosio J. P-300 rhythm detection using ANFIS algorithm and wavelet feature extraction in EEG signals. In: Proceedings of the World Congress on Engineering and Computer Science, October 20–22, 2010. San Francisco, CA: IAENG, 2010.Suche in Google Scholar
11. Clemente M, Rodriguez A, Rey B, Alcaniz M. Assessment of the influence of navigation control and screen size on the sense of presence in virtual reality using EEG. Expert Syst Appl 2014;41:1584–92.10.1016/j.eswa.2013.08.055Suche in Google Scholar
12. Mayaud L, Congedo M, Van Laghenhove A, Orlikowski D, Figere M, Azabou E, et al. A comparison of recording modalities of P300 event-related potentials (ERP) for brain-computer interface (BCI) paradigm. Neurophysiol Clin 2013;43:217–27.10.1016/j.neucli.2013.06.002Suche in Google Scholar PubMed
13. Wojcik GM. Quality of plastic used in sensors, 2013. Available from: http://www.emotiv.com/forum/messages/forum14/topic3665/message16825/. Accessed 12 July 2015.Suche in Google Scholar
14. Portelli AJ, Daly I, Spencer M, Nasuto SJ. Low cost brain computer interface: first results. In: Proceedings of the 5th International Brain-Computer Interface Conference, September 22–24, 2011. Graz, Austria: Verlag der Technischen Universität, 2011:320–3.Suche in Google Scholar
15. Tadeusiewicz R, Rotter P. From cochlear implants to brain-computer interfaces. Bio-Algorithms Med-Syst 2012;8: 267–86.10.2478/bams-2012-0022Suche in Google Scholar
16. Mikołajewska E, Mikołajewski D. The prospects of brain – computer interface applications in children. Cent Eur J Med 2014;9:74–9.10.2478/s11536-013-0249-3Suche in Google Scholar
17. Mikołajewska E, Mikołajewski D. Ethical considerations in the use of brain-computer interfaces. Cent Eur J Med 2013;8:720–4.10.2478/s11536-013-0210-5Suche in Google Scholar
18. Kawala-Janik A, Baranowski J, Podpora M, Piatek P, Pelc M. Use of a cost effective neuroheadset Emotiv Epoc for pattern recognition purposes. Int J Comput 2014;13:1–8.10.47839/ijc.13.1.618Suche in Google Scholar
©2015 by De Gruyter
Artikel in diesem Heft
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Artikel in diesem Heft
- Frontmatter
- Reviews
- Genetic background of urinary incontinence – state-of-the-art and perspectives
- Modeling of metabolic diseases – a review of selected methods
- Research Articles
- Evaluation of Emotiv EEG neuroheadset
- Experimental research and modeling of selected layers of the trachea based on EBUS
- Application of S-transform to signal analysis
- The application of spherical standards for the evaluation of the accuracy of mapping shape in computed tomography
- Branched iterated function system (IFS) models with positioners for biological visualizations
- Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets
- Research on dynamics of the knee joint for different types of loads
- Modeling of lumbar spine equipped with fixator
