Abstract
The ability to feel, adapt, reason, remember and communicate makes human a social being. Disabilities limit opportunities and capabilities to socialize. With the recent advancement in brain-computer interface (BCI) technology, researchers are exploring if BCI can be augmented with human computer interaction (HCI) to give a new hope of restoring independence to disabled individuals. This motivates us to lay down our research objective, which is as follows. In this study, we propose to work with a hands-free text entry application based on the brain signals, for the task of communication, where the user can select a letter or word based on the intentions of left or right hand movement. The two major challenges that have been addressed are (i) interacting with only two imagery signals (ii) how a low-quality, noisy EEG signal can be competently processed and classified using novel combination of feature set to make the interface work efficiently. The results of five able-bodied users show that the error rate per minute is significantly reduced and it also illustrates that it can be further used to develop better BCI augmented HCI systems.
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References
Microsoft, Type without using the keyboard (On-Screen Keyboard). https://support.microsoft.com/en-in/help/10762/windows-use-on-screen-keyboard
Emotiv EPOC, Software Development Kit (2010). http://www.emotiv.com/researchers
World report on disability, World Health Organization (2011). http://www.who.int/disabilities/world_report/2011/report/en
Alomari, M.H., Samaha, A., AlKamha, K.: Automated classification of L/R hand movement EEG signals using advanced feature extraction and machine learning. Int. J. Adv. Comput. Sci. Appl. 4(6) (2013)
Bin, G., Gao, X., Wang, Y., Li, Y., Hong, B., Gao, S.: A high-speed BCI based on code modulation VEP. J. Neural Eng. 8(2), 025015 (2011)
Crow, K.L.: Four types of disabilities: their impact on online learning. TechTrends 52(1), 51–55 (2008)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988)
Graimann, B., Allison, B., Pfurtscheller, G.: Brain-computer interfaces: a gentle introduction. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds.) Brain-Computer Interfaces, pp. 1–27. Springer, Heidelberg (2009)
Guo, L., Wu, Y., Zhao, L., Cao, T., Yan, W., Shen, X.: Classification of mental task from EEG signals using immune feature weighted support vector machines. IEEE Trans. Magn. 47(5), 866–869 (2011)
Long, J., Li, Y., Wang, H., Yu, T., Pan, J., Li, F.: A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans. Neural Syst. Rehabil. Eng. 20(5), 720–729 (2012)
Prasad, G., Herman, P., Coyle, D., McDonough, S., Crosbie, J.: Using motor imagery based brain-computer interface for post-stroke rehabilitation. In: 2009 4th International IEEE/EMBS Conference on Neural Engineering, pp. 258–262. IEEE (2009)
Renard, Y., Lotte, F., Gibert, G., Congedo, M., Maby, E., Delannoy, V., Bertrand, O., Lécuyer, A.: Openvibe: an open-source software platform to design, test, and use brain-computer interfaces in real and virtual environments. Presence 19(1), 35–53 (2010)
Ryan, D.B., Frye, G., Townsend, G., Berry, D., Mesa-G, S., Gates, N.A., Sellers, E.W.: Predictive spelling with a P300-based brain-computer interface: increasing the rate of communication. Int. J. Hum. Comput. Interact. 27(1), 69–84 (2010)
Scherer, R., Muller, G., Neuper, C., Graimann, B., Pfurtscheller, G.: An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans. Biomed. Eng. 51(6), 979–984 (2004)
Spalteholz, L., Li, K.F., Livingston, N., Hamidi, F.: Keysurf: a character controlled browser for people with physical disabilities. In: Proceedings of the 17th international conference on World Wide Web, pp. 31–40. ACM (2008)
Wolpaw, J., Wolpaw, E.W.: Brain-Computer Interfaces: Principles and Practice. OUP USA, New York (2012)
Yong, X., Fatourechi, M., Ward, R.K., Birch, G.E.: The design of a point-and-click system by integrating a self-paced brain-computer interface with an eye-tracker. IEEE J. Emerg. Sel. Topics Circ. Syst. 1(4), 590–602 (2011)
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S.R., S. et al. (2017). BCI Augmented Text Entry Mechanism for People with Special Needs. In: Basu, A., Das, S., Horain, P., Bhattacharya, S. (eds) Intelligent Human Computer Interaction. IHCI 2016. Lecture Notes in Computer Science(), vol 10127. Springer, Cham. https://doi.org/10.1007/978-3-319-52503-7_7
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DOI: https://doi.org/10.1007/978-3-319-52503-7_7
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