User-centered design of brain-computer interfaces: and BCI Appliance

Open access


Brain-Computer Interface (BCI) allows for non-muscular communication with external world, which may be the only way of communication for patients in a locked-in state. This paper presents a complete software framework for BCI, a novel hardware solution for stimuli rendering in BCIs based on Steady State Visual Evoked Potentials (SSVEP), and a univariate algorithm for detection of SSVEP in the EEG time series. OpenBCI is a complete software framework for brain-computer interfaces. Owing to an open license and modular architecture, it allows for flexible implementations of different communication channels in the serial or parallel hybrid mode, minimization of costs and improvements of stability and efficiency. Complete software is freely available from BCI Appliance is a hardware solution that allows for dynamic control of menus with stable generation of stimuli for the SSVEP paradigm. The novelty consists of a design, whereby the LCD screen is illuminated from behind using an array of LEDs. Design pioneers also proposed a new line of thought about the user-centered design of BCI systems: a simple box with one on/off button, minimum embedded software, wireless connections to domotic and EEG acquisition devices, and user-controlled mode switching in a hybrid BCI.

[1] J.-D. Bauby, The Diving Bell and the Butterfly, Vintage, London, 2007.

[2] J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan, “Brain-computer interfaces for communication and control”, Clinical Neurophysiology 113 (6), 767-791 (2002).

[3] M. Nicolelis, Beyond Boundaries: the New Neuroscience ofConnecting Brains with Machines - and How it Will ChangeOur Lives, Times Books, London, 2011.

[4] F. Vialatte, M. Maurice-Vialatte, J.H. Dauwels, and A. Cichocki, “Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives”, Progress in Neurobiology 90 (4), 418-438 (2010).

[5] A. Materka and M. Byczuk, “Alternate half-field stimulation technique for ssvep-based brain-computer interfaces”, ElectronicsLetters 42, 321-327 (2006).

[6] S.G. Mason and G.E. Birch, ‘A general framework for braincomputer interface design”, Neural Systems and RehabilitationEngineering, IEEE Trans. 11 (1), 70-85 (2003).

[7], 2009.

[8] G. Pfurtscheller, B.Z. Allison, G. Bauernfeind, C. Brunner, T. Solis Escalante, R. Scherer, T.O. Zander, G. Mueller-Putz, C. Neuper, and N. Birbaumer, “The hybrid BCI”, Frontiers inNeuroscience 4, 42-57 (2010).

[9] G.R. Mueller-Putz, C. Breitwieser, M. Tangermann, M. Schreuder, M. Tavella, R. Leeb, F. Cincotti, F. Leotta, and C. Neuper, “TOBI hybrid BCI: Principle of a new assistive method”, Int. J. Bioelectromagnetism 13 (3), 144-145 (2011).

[10] J.d.R. Mill´an, R. Rupp, G.R. Mueller-Putz, R. Murray-Smith, C. Giugliemma, M. Tangermann, C. Vidaurre, F. Cincotti, A. K¨ubler, C. Neuper R. Leeb, K.R. M¨uller, and D. Mattia, “Combining brain-computer interfaces and assistive technologies: State-of-the-art and challenges”, Frontiers in Neuroscience 4, 161 (2010).

[11] C.S. Herrmann, “Human EEG responses to 1-100 hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena”, Experimental Brain Research 137 (3-4), 346-353 (2001).

[12] M. Byczuk, P. Poryzała, and A. Materka, “On possibility of stimulus parameter selection for ssvep-based brain-computer interface”, in Man-Machine Interactions 2, eds. T. Czachorski, S. Kozielski, and U. Stanczyk, vol. 103, pp. 57-64, Springer, Berlin, 2011.

[13] J. Mueller-Gerking, G. Pfurtscheller, and H. Flyvbjerg, “Designing optimal spatial filters for single-trial EEG classification in a movement task”, Clinical Neurophysiology 110 (5), 787- 798 (1999).

[14] Y. Zhang, G. Zhou, Q. Zhao, A. Onishi, J. Jin, X. Wang, and A. Cichocki, “Multiway canonical correlation analysis for frequency components recognition in SSVEP-based BCIs”, ICONIP 1 (11), 287-295 (2011).

Bulletin of the Polish Academy of Sciences Technical Sciences

The Journal of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 1.156
5-year IMPACT FACTOR: 1.238

CiteScore 2016: 1.50

SCImago Journal Rank (SJR) 2016: 0.457
Source Normalized Impact per Paper (SNIP) 2016: 1.239

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