Abstract
We present a novel approach for providing people with severe impairment the possibility of collecting feedbacks and communication prompts using a Brain Computer Interface (BCI). In particular we implemented an experimental apparatus, based on MindWave; the latter is a low cost and well known BCI device released by NeuroSky®, based on a dry single electrode and designed for enhancing Human-Computer Interaction (HCI), especially for videogames [8]. Even if BCI is an emerging research area and appears to be still relatively immature, the expected future impact on HCI is really considerable.
The experimental apparatus allows users to interact with the computer only through their brain biological signals, without the need for using muscles. The BCI system is based on the Steady–State Visual Evoked Potential (SSVEP). The SSVEP is generated in presence of a repetitive visual stimuli; in our experiment two different visual stimuli are available: when the patient looks at one of the stimuli the SVVEP signal is generated and she/he can express a decision or respond to a question.
Even if the experiment has been tested only on a very limited patient set, the results are extremely promising.
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Website source of the image, http://www.bci2000.org (accessed on February 28, 2014)
Cecotti, H., Volosyak, I., Graser, A.: Reliable visual stimuli on LCD screens for SSVEP based BCI. In: Proc. of the 18th European Signal Processing Conference (EUSIPCO 2010). p. 5 pages. Aalborg, Danemark, This research was fully supported within the 6th European Community Framework Program by a Marie Curie European ToK grant BrainRobot, MTKD-CT-2004-014211 and within the 7th European Community Framework Program by a Marie Curie European Re-Integration Grant RehaBCI, PERG02-GA-2007-224753 and by an EU ICT grant BRAIN, ICT-2007-224156 (August 2010), http://hal.archives-ouvertes.fr/hal-00536125
Guneysu, A., Akin, H.: An ssvep based bci to control a humanoid robot by using portable eeg device. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6905–6908 (July 2013)
Kuś, R., Duszyk, A., Milanowski, P., Łabęcki, M., Bierzyńska, M., Radzikowska, Z., Michalska, M., Zygierewicz, J., Suffczynski, P., Durka, P.J.: On the quantification of ssvep frequency responses in human eeg in realistic bci conditions. PLoS ONE 8, 1–9 (2013)
Lin, Y.P., Wang, Y., Jung, T.P.: A mobile ssvep-based brain-computer interface for freely moving humans: The robustness of canonical correlation analysis to motion artifacts. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (2013)
Liu, Y., Jiang, X., Cao, T., Wan, F., Mak, P.U., Mak, P.I., Vai, M.I.: Implementation of ssvep based bci with emotiv epoc. In: 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), pp. 34–37 (July 2012)
Luo, A., Sullivan, T.J.: A user-friendly ssvep-based brain computer interface using a time-domain classifier. Journal of Neural Engineering 7(2), 026010 (2010), http://stacks.iop.org/1741-2552/7/i=2/a=026010
The neurosky biosensors website:, http://neurosky.com/products-markets/eeg-biosensors/ (accessed on February 28, 2014)
The neurosky mindset website, http://developer.neurosky.com/docs/doku.php?id=mindset_instruction_manual (accessed on February 28, 2013)
Resalat, S.N., Setarehdan, S.K.: An improved ssvep based bci system using frequency domain feature classification. American Journal of Biomedical Engineering 3, 1–8 (2013)
Seyed, N.R., Seyed, K.S.: An improved ssvep based bci system using frequency domain feature classification. American Journal of Biomedical Engineering 3, 1–8 (2013)
Stamatto Ferreira, A.L., Cunha de Miranda, L., Gomes Sakamoto, S.: A survey of interactive systems based on brain - computer interfaces. SBC Journal on 3D Interactive Systems 4, 3–12 (2013)
Wang, Y., Gao, X., Hong, B., Gao, S.: Practical designs of brain computer interfaces based on the modulation of eeg rhythms. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds.) Brain-Computer Interfaces, The Frontiers Collection, pp. 137–154. Springer, Heidelberg (2010), http://dx.doi.org/10.1007/978-3-642-02091-9_8
Zhu, D., Bieger, J., Molina, G.G., Aarts, R.M.: A survey of stimulation methods used in ssvep-based bcis. Intell. Neuroscience 2010, 1:1–1:12 (2010), http://dx.doi.org/10.1155/2010/702357
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Gervasi, O., Magni, R., Macellari, S. (2014). A Brain Computer Interface for Enhancing the Communication of People with Severe Impairment. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_53
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DOI: https://doi.org/10.1007/978-3-319-09153-2_53
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