Home Investigation of the mirrored-word reading paradigm for BCI implementation
Article
Licensed
Unlicensed Requires Authentication

Investigation of the mirrored-word reading paradigm for BCI implementation

  • Randy E.S. Harnarinesingh EMAIL logo and Chanan S. Syan
Published/Copyright: June 27, 2018

Abstract

Brain-computer interface (BCI) applications such as keyboard control and vehicular navigation present significant assistive merit for disabled individuals. However, there are limitations associated with BCI paradigms which restrict a wider adoption of BCI technology. For example, rapid serial visual presentation (RSVP) paradigms can induce seizures in photosensitive epileptic subjects. This paper evaluates the novel mirrored-word reading paradigm (MWRP) for BCI implementation using an offline experimental study. The offline study obtained an average single-trial classification accuracy of 74.10%. The results also demonstrate that the use of multiple trials for classification can increase the accuracy as is common with BCIs. The developed MWRP-based BCI also utilized a low presentation frequency which averts the possibility of paradigm induced photosensitivity. However, there are multiple avenues for future work. The MWRP can be implemented in the online format for real-time device control. For example, a vehicular application platform can be used where the word orientation represents directions for travel. The MWRP can also be investigated across a wider range of stimulus presentation parameters such as timing, color and stimulus size. Such studies can be used to suggest further improvements to the paradigm which can enhance its applicability for online device control.

  1. Author Statement

  2. Research funding: Authors state no funding involved.

  3. Conflict of interest: No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript.

  4. Informed consent: Written informed consent has been obtained from all individuals.

  5. Ethical approval: The research related to human use complied with all the relevant national regulations and institutional policies and was performed in accordance to the tenets of the Declaration of Helsinki and has been approved by the author’s institutional review board or equivalent committee.

References

[1] Citi L, Poli R, Cinel C, Sepulveda F. P300-based BCI mouse with genetically-optimized analogue control. IEEE Trans Neural Syst Rehabil Eng 2008;16:51–61.10.1109/TNSRE.2007.913184Search in Google Scholar PubMed

[2] Muller SMT, Bastos-Filho TF, Sarcinelli-Filho M. Using a SSVEP-BCI to command a robotic wheelchair. In: Industrial Electronics (ISIE). New York, USA: IEEE International Symposium; 2011:957–62.10.1109/ISIE.2011.5984288Search in Google Scholar

[3] Scherer R, Muller GR, Neuper C, Graimann B, Pfurtscheller G. An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans Bio-Med Eng 2004;51:979–84.10.1109/TBME.2004.827062Search in Google Scholar

[4] Sun G, Li K, Li X, Zhang B, Yuan S, Wu G. A general framework of brain-computer interface with visualization and virtual reality feedback. In: Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing. New York, USA: IEEE; 2009:418–23.10.1109/DASC.2009.72Search in Google Scholar

[5] Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clinl Neurophysiol 1988;70:510–23.10.1016/0013-4694(88)90149-6Search in Google Scholar

[6] Regan D. Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. New York, NY: Elsevier; 1989.Search in Google Scholar

[7] Fabien L, Anatole L, Fabrice L, Bruno A. Studying the use of fuzzy inference systems for motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 2007;15:322–4.10.1109/TNSRE.2007.897032Search in Google Scholar PubMed

[8] Burke DP, Kelly SP, de Chazal P, Reilly RB, Finucane C. A parametric feature extraction and classification strategy for brain-computer interfacing. IEEE Trans Neural Syst Rehabil Eng 2005;13:12–7.10.1109/TNSRE.2004.841881Search in Google Scholar PubMed

[9] Oweis RJ, Hamdi N, Ghazali A, Lwissy K. A comparison study on machine learning algorithms utilized in P300-based BCI. J Health Med Inform 2013;4:2.10.4172/2157-7420.1000126Search in Google Scholar

[10] Yin E, Zhou Z, Jiang J, Chen F, Liu Y, Hu D. A novel hybrid BCI speller based on the incorporation of SSVEP into the P300 paradigm. J Neural Eng 2013;10:026012.10.1088/1741-2560/10/2/026012Search in Google Scholar PubMed

[11] Lu J, Speier W, Hu X, Pouratian N. The effects of stimulus timing features on P300 speller performance. Clin Neurophysiol 2013;124:306–14.10.1016/j.clinph.2012.08.002Search in Google Scholar PubMed PubMed Central

[12] He S, Zhang R, Wang Q, Chen Y, Yang T, Feng Z, et al. A p300-based threshold-free brain switch and its application in wheelchair control. IEEE Trans Neural Syst Rehabil Eng 2017;25:715–25.10.1109/TNSRE.2016.2591012Search in Google Scholar PubMed

[13] Gannouni S, Alangari N, Mathkour H, Aboalsamh H, Belwafi K. BCWB: a P300 brain-controlled web browser. Int J Semant Web Inf Syst 2017;13:55–73.10.4018/IJSWIS.2017040104Search in Google Scholar

[14] Speier W, Chandravadia N, Roberts D, Pendekanti S, Pouratian N. Online BCI typing using language model classifiers by ALS patients in their homes. Brain-Computer Interfaces 2017;4:114–21.10.1080/2326263X.2016.1252143Search in Google Scholar PubMed PubMed Central

[15] Muller-Putz GR, Scherer R, Neuper C, Pfurtscheller G. Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? IEEE Trans Neural Syst Rehabil Eng 2006;14:30–7.10.1109/TNSRE.2005.863842Search in Google Scholar PubMed

[16] Punsawad Y, Wongsawat Y. On the enhancement of training session performance via attention for single-frequency/multi-commands based steady state auditory evoked potential BCI. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. New York, USA: IEEE; 2012:1761–64.10.1109/EMBC.2012.6346290Search in Google Scholar PubMed

[17] Friman O, Volosyak I, Graser A. Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Trans Bio-Med Eng 2007;54:742–50.10.1109/TBME.2006.889160Search in Google Scholar PubMed

[18] Wang D, Kobayashi T, Cui G, Watabe D, Cao J. Real-time mobile phone dialing system based on SSVEP. Proc. SPIE 2017;10341:103410R. doi: 10.1117/12.2268404.Search in Google Scholar

[19] Meriño L, Nayak T, Kolar P, Hall G, Mao Z, Pack DJ. Asynchronous control of unmanned aerial vehicles using a steady-state visual evoked potential-based brain computer interface. Brain-Computer Interfaces 2017;4:122–35.10.1080/2326263X.2017.1292721Search in Google Scholar

[20] Chen J, Zhang D, Engel AK, Gong Q, Maye A. Application of a single-flicker online SSVEP BCI for spatial navigation. PLoS One 2017;12:e0178385.10.1371/journal.pone.0178385Search in Google Scholar PubMed PubMed Central

[21] MacVicar BA. Disinhibition and brain rhythms. J Physiol 1997;500:283.10.1113/jphysiol.1997.sp022018Search in Google Scholar PubMed PubMed Central

[22] Pineda JA, Allison BZ, Vankov A. The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RPs): toward a brain-computer interface (BCI). IEEE Trans Rehabil Eng 2000;8:219–22.10.1109/86.847822Search in Google Scholar

[23] Suffczynski P, Pijn JPM, Pfurtscheller G, Lopes da Silva F. 1999. Event-related dynamics of alpha band rhythms: a neuronal network model of focal ERD-surround ERS. In: Event-related desynchronization. Handbook of Electroencephalography and Clinical Neuorphysiology, Revised Series, vol. 6. Amsterdam: Elsevier Science; 1999:67–85, pp. 406. http://hdl.handle.net/11245/1.159182.Search in Google Scholar

[24] Wang Y, Hong B, Gao X, Gao S. Design of electrode layout for motor imagery based brain-computer interface. Electron Lett 2007;43:557–8.10.1049/el:20070563Search in Google Scholar

[25] Frolov A, Húsek D, Biryukova E, Bobrov P, Mokienko O, Alexandrov A. Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain-computer interface based on motor imagery. Neural Netw World 2017;27:107.10.14311/NNW.2017.27.006Search in Google Scholar

[26] Ron-Angevin R, Velasco-Álvarez F, Fernández-Rodríguez Á, Díaz-Estrella A, Blanca-Mena MJ, Vizcaíno-Martín FJ. Brain-computer interface application: auditory serial interface to control a two-class motor-imagery-based wheelchair. J Neuroeng Rehabil 2017;14:49.10.1186/s12984-017-0261-ySearch in Google Scholar PubMed

[27] Hinterberger T, Weiskopf N, Veit R, Wilhelm B, Betta E, Birbaumer N. An EEG-driven brain-computer interface combined with functional magnetic resonance imaging (fMRI). IEEE Trans Bio-med Eng 2004;51:971–4.10.1109/TBME.2004.827069Search in Google Scholar

[28] Schalk G, McFarland DJ, Hinterberger T, Birbaumer N, Wolpaw JR. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans Bio-Med Eng 2004;51:1034–43.10.1109/TBME.2004.827072Search in Google Scholar

[29] Krauledat M, Dornhege G, Blankertz B, Losch F, Curio G, Muller KR. Improving speed and accuracy of brain-computer interfaces using readiness potential features. In: Engineering in Medicine and Biology Society. 26th Annual International Conference of the IEEE; 2004:4511–5.10.1109/IEMBS.2004.1404253Search in Google Scholar

[30] Raymond JE, Shapiro KL, Arnell KM. Temporary suppression of visual processing in an RSVP task: an attentional blink? J Exp Psychol Hum Percept Perform 1992;18:849.10.1037/0096-1523.18.3.849Search in Google Scholar

[31] Salvaris M, Sepulveda F. Perceptual errors in the Farwell and Donchin matrix speller. In: 4th International IEEE/EMBS Conference on Neural Engineering. New York, NY: IEEE; 2009:275–8.10.1109/NER.2009.5109286Search in Google Scholar

[32] Kanwisher NG. Repetition blindness: type recognition without token individuation. Cognition 1987;27:117–43.10.1016/0010-0277(87)90016-3Search in Google Scholar PubMed

[33] Fazel-Rezai R. Human error in P300 speller paradigm for brain-computer interface. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. New York, NY: IEEE; 2007:2516–19.10.1109/IEMBS.2007.4352840Search in Google Scholar PubMed

[34] Fisher RS, Harding G, Erba G, Barkley GL, Wilkins A. photic-and pattern-induced seizures: a review for the Epilepsy Foundation of America Working Group. Epilepsia 2005;46:1426–41.10.1111/j.1528-1167.2005.31405.xSearch in Google Scholar PubMed

[35] Wang Y, Wang R, Gao X, Hong B, Gao S. A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 2006;14:234–40.10.1109/TNSRE.2006.875576Search in Google Scholar PubMed

[36] Pfurtscheller G, Scherer R. Brain-computer interfaces used for virtual reality control. In: ICABB. 2010. https://graz.pure.elsevier.com/de/publications/brain-computer-interfaces-used-for-virtual-reality-control.10.5772/13467Search in Google Scholar

[37] Pires G, Castelo-Branco M, Nunes U. Visual P300-based BCI to steer a wheelchair: a Bayesian approach. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. New York, NY: IEEE; 2008:658–61.10.1109/IEMBS.2008.4649238Search in Google Scholar PubMed

[38] Dickhaus T, Sannelli C, Müller K-R, Curio G, Blankertz B. Predicting BCI performance to study BCI illiteracy. BMC Neurosci 2009;10(Suppl 1):P84. https://doi.org/10.1186/1471-2202-10-S1-P84.10.1186/1471-2202-10-S1-P84Search in Google Scholar

[39] Jin J, Allison BZ, Sellers EW, Brunner C, Horki P, Wang X, et al. An adaptive P300-based control system. J Neural Eng 2011;8:1–14.10.1088/1741-2560/8/3/036006Search in Google Scholar PubMed PubMed Central

[40] Townsend G, LaPallo B, Boulay C, Krusienski D, Frye G, Hauser C, et al. A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns. Clin Neurophysiol 2010;121:1109–20.10.1016/j.clinph.2010.01.030Search in Google Scholar PubMed PubMed Central

[41] Townsend G, Shanahan J, Ryan DB, Sellers EW. A general P300 brain-computer interface presentation paradigm based on performance guided constraints. Neurosci Lett 2012;531:63–8.10.1016/j.neulet.2012.08.041Search in Google Scholar PubMed PubMed Central

[42] Proverbio AM, Wiedemann F, Adorni R, Rossi V, Del Zotto M, Zani A. Dissociating object familiarity from linguistic properties in mirror word reading. Behav Brain Funct 2007;3:43.10.1186/1744-9081-3-43Search in Google Scholar PubMed PubMed Central

[43] Rudell AP, Hu B, Prasad S, Andersons PV. The recognition potential and reversed letters. Int J Neurosci 2000;101:109–31.10.3109/00207450008986496Search in Google Scholar PubMed

[44] Zhang Y, Qiu J, Huang H, Zhang Q, Bao B. Chinese character recognition in mirror reading: evidence from event-related potential. Int J Psychol 2009;44:360–8.10.1080/00207590802500190Search in Google Scholar PubMed

[45] Rossion B, Joyce CA, Cottrell GW, Tarr MJ. Early lateralization and orientation tuning for face, word, and object processing in the visual cortex. Neuroimage 2003;20:1609–24.10.1016/j.neuroimage.2003.07.010Search in Google Scholar PubMed

[46] Vidaurre C, Schlogl A, Cabeza R, Scherer R, Pfurtscheller G. Study of on-line adaptive discriminant analysis for EEG-based brain computer interfaces. IEEE Trans Bio-Med Eng 2007;54:550–6.10.1109/TBME.2006.888836Search in Google Scholar PubMed

[47] Bigdely-Shamlo N, Vankov A, Ramirez RR, Makeig S. Brain activity-based image classification from rapid serial visual presentation. IEEE Trans Neural Syst Rehabil Eng 2008;16: 432–41.10.1109/TNSRE.2008.2003381Search in Google Scholar PubMed

[48] Boksem MA, Meijman TF, Lorist MM. Effects of mental fatigue on attention: an ERP study. Cogn Brain Res 2005;25:107–16.10.1016/j.cogbrainres.2005.04.011Search in Google Scholar PubMed

[49] Lorist MM, Boksem MA, Ridderinkhof KR. Impaired cognitive control and reduced cingulate activity during mental fatigue. Cogn Brain Res 2005;24:199–205.10.1016/j.cogbrainres.2005.01.018Search in Google Scholar PubMed

[50] Lorist MM, Klein M, Nieuwenhuis S, Jong R, Mulder G, Meijman TF. Mental fatigue and task control: planning and preparation. Psychophysiology 2000;37:614–25.10.1111/1469-8986.3750614Search in Google Scholar PubMed

[51] LaPray M, Ross R. The graded word list: quick gauge of reading ability. J Reading 1969;12:305–7.Search in Google Scholar

[52] Hoffmann U, Vesin JM, Ebrahimi T, Diserens K. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods 2008;167:115–25.10.1016/j.jneumeth.2007.03.005Search in Google Scholar PubMed

[53] Ferdjallah M, Barr RE. Adaptive digital notch filter design on the unit circle for the removal of powerline noise from biomedical signals. IEEE Trans Bio-Med Eng 1994;41:529–36.10.1109/10.293240Search in Google Scholar PubMed

[54] Moore AW. Cross-validation for detecting and preventing overfitting. School of Computer Science. Carneigie Mellon University; 2001. https://clm.utexas.edu/fietelab/QuantNeuro/readings/crossvalidation_slides_Moore_CMU.pdf.Search in Google Scholar

[55] Abidine MB, Fergani B. Evaluating C-SVM, CRF and LDA classification for daily activity recognition. In: International Conference on Multimedia Computing and Systems. New York, USA: IEEE; 2012:272–7. DOI: 10.1109/ICMCS.2012.6320300.Search in Google Scholar

[56] Kowler E, Anton S. Reading twisted text: implications for the role of saccades. Vision Res 1987;27:45–60.10.1016/0042-6989(87)90142-8Search in Google Scholar PubMed

Received: 2017-12-04
Accepted: 2018-06-01
Published Online: 2018-06-27
Published in Print: 2019-05-27

©2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 20.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/bmt-2017-0223/html
Scroll to top button