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Evaluation of Emotiv EEG neuroheadset

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
211--215
Opis fizyczny
Bibliogr. 18 poz., rys., zdj.
Twórcy
  • University of Economics and Innovation, Projektowa 4, 20-209 Lublin, Poland
  • University of Economics and Innovation, Lublin, Poland
autor
  • University of Economics and Innovation, Lublin, Poland
Bibliografia
  • 1. Jasper JH. The ten-twenty electrode system of the international federation. Electroen Clin Neuro 1958;10:371–5.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 8. Louwerse M, Hutchinson S. Neurological evidence linguistic processes precede perceptual simulation in conceptual processing. Front Psychol 2012;3:1–11.
  • 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. 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 15. Tadeusiewicz R, Rotter P. From cochlear implants to brain-computer interfaces. Bio-Algorithms Med-Syst 2012;8: 267–86.
  • 16. Mikołajewska E, Mikołajewski D. The prospects of brain – computer interface applications in children. Cent Eur J Med 2014;9:74–9.
  • 17. Mikołajewska E, Mikołajewski D. Ethical considerations in the use of brain-computer interfaces. Cent Eur J Med. 2013;8:720–4.
  • 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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-db950846-cc36-44b0-8587-6411d6727501
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