Abstract
Innovative methods and new technologies have significantly improved the quality of our daily life. However, disabled people, for example those that cannot use their arms and legs anymore, often cannot benefit from these developments, since they cannot use their hands to interact with traditional interaction methods (such as mouse or keyboard) to communicate with a computer system. A brain–computer interface (BCI) system allows such a disabled person to control an external device via brain waves. Past research mostly dealt with static interfaces, which limit users to a stationary location. However, since we are living in a world that is highly mobile, this paper evaluates a speller interface on a mobile phone used in a moving condition. The spelling experiments were conducted with 14 able-bodied subjects using visual flashes as the stimulus to spell 47 alphanumeric characters (38 letters and 9 numbers). This data was then used for the classification experiments. In par- ticular, two research directions are pursued. The first investigates the impact of different classification algorithms, and the second direction looks at the channel configuration, i.e., which channels are most beneficial in terms of achieving the highest classification accuracy. The evaluation results indicate that the Bayesian Linear Discriminant Analysis algorithm achieves the best accuracy. Also, the findings of the investigation on the channel configuration, which can potentially reduce the amount of data processing on a mobile device with limited computing capacity, is especially useful in mobile BCIs.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11517-017-1658-2/MediaObjects/11517_2017_1658_Fig1_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11517-017-1658-2/MediaObjects/11517_2017_1658_Fig2_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11517-017-1658-2/MediaObjects/11517_2017_1658_Fig3_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11517-017-1658-2/MediaObjects/11517_2017_1658_Fig4_HTML.gif)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11517-017-1658-2/MediaObjects/11517_2017_1658_Fig5_HTML.gif)
Similar content being viewed by others
References
Sutton S, Braren M, Zubin J, John ER (1965) Evoked-potential correlates of stimulus uncertainty. Science 150(3700):1187– 1188
Farwell LA, Donchin E (1988) Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70(6):510–523
Guger C, Daban S, Sellers E, Holzner C, Krausz G, Carabalona R (2009) How many people are able to control a P300-based brain-computer interface (BCI)? Neurosci Lett 462(1):94–98
Townsend G, LaPallo BK, Boulay CB, Krusienski DJ, Frye GE, Hauser CK (2010) A novel P300-based brain–computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 121(7):1109–1120
Fazel-Rezai R, Abhari K (2009) A region-based p300 speller for brain-computer interface. Can J Elect Comput E 34(3):81– 85
Obeidat Q, Campbell T, Kong J (2013) The zigzag paradigm: a new P300-based brain computer interface. In: Proc. of the 15th ACM on ICMI. ACM, pp 205–212
Lin CT, Chen YC, Huang TY, Chiu TT, Ko LW, Liang SF (2008) Development of wireless brain computer interface with embedded multitask scheduling and its application on real-time driver’s drowsiness detection and warning. IEEE T Bio-Med Eng 55(5):1582–1591
Wang YT, Wang Y, Jung TP (2010) A cell-phone-based brain-computer interface for communication in daily life. J Neural Eng 8(2):025018, 1–5
De Vos M, Kroesen M, Emkes R, Debener S (2014) P300 speller BCI with a mobile EEG system: comparison to a traditional amplifier. J Neural Eng 11(3):036008, 1–8
Debener S, Emkes R, De Vos M, Bleichner M (2015) Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Sci Rep 5:16743, 1–11
Debener S, Minow F, Emkes R, Gandras K, De Vos M (2012) How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology 49(11):1617–1621
De Vos M, Gandras K, Debener S (2014) Towards a truly mobile auditory brain–computer interface: exploring the p300 to take away. Int J Psychophysiol 91(1):46–53
Liao LD, Chen CY, Wang IJ, Chen SF, Li SY, Chen BW (2012) Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. J Neuroeng Rehabil 9(1):1–11
Gwin JT, Gramann K, Makeig S, Ferris DP (2010) Removal of movement artifact from high-density EEG recorded during walking and running. J Neurophysiol 103(6):3526–3534
Gramann K, Gwin JT, Bigdely-Shamlo N, Ferris DP, Makeig S (2010) Visual evoked responses during standing and walking. Neuroscience 4(202):1–12
Campbell A, Choudhury T, Hu S, Lu H, Mukerjee MK, Rabbi M (2010) 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. ACM, pp 3–8
Obeidat Q, Campbell T, Kong J (2015) Introducing the edges paradigm: a P300 brain–computer interface for spelling written words. IEEE Trans Hum Mach Syst 45(6):727–738
Obeidat Q (2014) Towards improving P300-based brain-computer interfaces: from desktop to mobile. PhD Dissertation, North Dakota State University
Colwell KA, Ryan DB, Throckmorton CS, Sellers EW, Collins LM (2014) Channel selection methods for the P300 Speller. J Neurosci Methods 232:6–15
Lang M (2012) Investigating the Emotiv EPOC for cognitive control in limited training time. Honors Report, University of Canterbury
EmotivSystem Emotiv: brain-computer interface technology. http://www.emotiv.com [Online; Accessed May 5 2016]
Sharbrough F, Chatrian GE, Lesser RP, Luders H, Nuwer M, Picton TW (1991) American electroencephalographic society guidelines for standard electrode position nomenclature. J Clin Neurophysiol 8 (2):200–202
Serby H, Yom-Tov E, Inbar GF (2005) An improved p300-based braincomputer interface. IEEE T Neur Sys Reh 13(1):89–98
Hoffmann U, Vesin J-M, Ebrahimi T, Diserens K (2008) An efficient p300-based brain-computer interface for disabled subjects. J Neurosci Meth 167(1):115–125
Lotte F, Congedo M, Lecuyer A, Lamarche F, Arnaldi B (2007) A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng
Krusienski DJ, Sellers EW, Cabestaing F (2006) A comparison of classification techniques for the P300 Speller. J Neural Eng 3(4):299–305
Panicker RC, Puthusserypady S, Sun Y (2010) Adaptation in p300 braincomputer interfaces: a two-classifier cotraining approach. IEEE Trans Biomed Eng 57(12):2927–2935
Mirghasemi H, Fazel-Rezai R, Shamsollahi MB (2006) Analysis of p300 classifiers in brain computer interface speller. In: Proceedings of the 28th annual international conference of the IEEE engineering in medicine and biology society (EMBS’06), pp 6205–6208
Nijboer F, Sellers EW, Mellinger J (2008) A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 119(8):1909–1916
Sellers EW, Donchin E (2006) A P300-based brain-computer interface: initial tests by ALS patients. Clin Neurophysiol 117(3):538–548
Hoffmann U, Vesin JM, Ebrahimi T, Diserens K (2008) An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods 167(1):115–125
Thulasidas M, Guan C, Wu J (2006) Robust classification of EEG signal for brain-computer interface. IEEE Transa Neural Syst Rehab Eng 14(1):24–29
Suykens J, Van Gestel T, De Brabanter J, De Moor B, Vanderwalle J (2002) Least square support vector machines. World Scientific, Singapore
Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2 (4):303–314
Palaniappan R (2005) Brain computer interface design using band powers extracted during mental tasks. In: Proceedings of the 2nd international IEEE EMBS conference on neural engineering (NER’05). Arlington
del R Millan J, Mourino J, Franze M, Cincotti F, Varsta M, Heikkonen J, Babiloni F (2002) A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans Neural Netw
Nakayaman K, Inagaki K (2006) A brain computer interface based on neural network with efficient pre-processing. In: Proceedings of the international symposium on intelligent signal processing and communications (ISPACS’06). Yonago
Haselsteiner E, Pfurtscheller G (2000) Using time-dependent neural networks for EEG classification. IEEE Trans Rehabil Eng
Millan JR, Mourino J (2003) Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project. IEEE Trans Neural Syst Rehabil Eng
Hornik K (1991) approximation capabilities of multilayer feedforward. Netw Neural Netw 4(2):251–257
Bengio Y, LeCun Y (2007) Scaling learning algorithms towards AI, large-scale kernel machines. In: L Bottou, O Chapelle, D DeCoste, J Weston (eds). MIT Press
Bengio Y, Delalleau O, Simard C (2010) Decision trees do not generalize to new variations. Comput Intell 26(4):449–467
Freund Y, Schapire RE (1997) A Decision-Theoretic generalization of On-Line learning and an application to boosting. J Comput Syst Sci 55(1):119–139
Schapire RE (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651– 1686
Seiffert C, Khoshgoftaar T, Hulse J, Napolitano A (2008) RUSBOost: improving classification performance when training data is skewed. In: 19th International conference on pattern recognition
Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337–407
Krusienski DJ, Sellers EW, McFarland DJ, Vaughan TM, Wolpaw JR (2008) Toward enhanced p300 speller performance? J Neurosci Methods 167(1):15–21
Schroeder M, Lal TN, Hinterberger T, Bogdan M, Hill NJ, Birbaumer N, Robust EEG (2005) Channel selection across subjects for brain–computer interfaces. EURASIP J Adv Signal Process 2005(19):3103–12
Rakotomamonjy A, Guigue V (2008) BCI competition III: dataset II-ensemble of SVMs for BCI P300 speller. IEEE Trans Biomed Eng 55(3):1147–54
Cecotti H, Rivet B, Congedo M, Jutten C (2011) A robust sensor selection method for p300 brain–computer interfaces. J Neural Eng 8(1):1–21
Lal TN, Schroeder M, Hinterberger T, Weston J, Bogdan M, Birbaumer N (2004) Support vector channel selection in BCI. IEEE Trans Biomed Eng 51(6):1003–10
Lan T, Erdogmus D, Adami A, Mathan S, Pavel M (2007) Channel selection and feature projection for cognitive load estimation using ambulatory EEG. Comput Intell Neurosci 2007:74895
Jin J, Allison BZ, Brunner C, Wang B, Wang X, Zhang J (2010) P300 Chinese input system based on Bayesian LDA. Biomed Tech (Berl) 55:5–18
Acknowledgements
The authors would like to thank Qasem Obeidat for performing the spelling experiments on which this research study is based on.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ludwig, S.A., Kong, J. Investigation of different classifiers and channel configurations of a mobile P300-based brain–computer interface. Med Biol Eng Comput 55, 2143–2154 (2017). https://doi.org/10.1007/s11517-017-1658-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-017-1658-2