ABSTRACT
In this study, we proposed a practical hybrid brain-computer interface (BCI) speller to enhance the performance for Chinese character input by incorporating eye tracker into the traditional P300 event-related potential spelling paradigm. The spelling process is activated asynchronously when subjects gazes at the Start-button. Then, the interface turns to the classic row-column P300 paradigm. A Chinese character can be inputted in three steps, i) input the initial consonant, ii) input the vowel component, and iii) selecting the target character. With the combination of an eye-tracker, the proposed paradigm achieves asynchronous operation, and achieves accurate and fast-reading Chinese character input without adding additional cognitive load compared to the traditional P300 method. The improved system showed an average speed of about 1.14 sinograms per minute. The experimental results revealed that the proposed paradigm can achieve character input asynchronously with a high accuracy and speed, improving the traditional P300-based spelling paradigm.
- D. B. Ryan, G. E. Frye, G. Townsend : Predictive spelling with a P300-based brain–computer interface: increasing the rate of communication. Int. J. Hum.-Comput. Int. 27(1), 69–84 (2010).Google ScholarCross Ref
- W. Speier, C. W. Arnold, N. Chandravadia, : Improving P300 spelling rate using language models and predictive spelling. Brain-Comput. Interfa. 5, 13-22 (2018).Google ScholarCross Ref
- B. O. Mainsah, K. A. Colwell, L. M. Collins, and C. S. Throckmorton: Utilizing a language model to improve online dynamic data collection in P300 spellers. IEEE Trans. Neural Syst. Rehabil. Eng. 22(4), 837-846 (2014).Google ScholarCross Ref
- G. Kshirsagar, N. Londhe: Weighted Ensemble of Deep Convolution Neural Networks for Single-Trial Character Detection in Devanagari-Script-Based P300 Speller. IEEE T. Cogn. Dev. Syst. 12(3) (2020).Google Scholar
- H. Cecotti, A. Graser: Convolutional neural networks for P300 detection with application to brain-computer interfaces. IEEE T. Pattern Anal. 33(3), 433-445 (2011).Google ScholarDigital Library
- M. Liu, W. Wu, Z. Gu, Z. Yu, F. Qi, Y. Li: Deep learning based on Batch Normalization for P300 signal detection. Neurocomputing 275, 288-297 (2018).Google ScholarDigital Library
- X. Liu, M. Jiang, C. Fei, C. Zhou, M. Shi: P300-Based Chinese Speller: A Systematic Summary. 4th Ita. 12, (2017).Google ScholarCross Ref
- H. Zhang, C. Guan, C. Wang: Asynchronous P300-based brain - Computer interfaces: A computational approach with statistical models. IEEE T. Bio.-Med. Eng. 55(6), 1754-1763 (2008).Google ScholarCross Ref
- M. Cagigal, G. Pilar, A. Daniel, H. Roberto: An Asynchronous P300-Based Brain-Computer Interface Web Browser for Severely Disabled People. IEEE T. Neur. Sys. Reh. 25(8), 1332-1342 (2017).Google Scholar
- F. Aloise, F. Schettini, P. Aricò, F. Leotta, S. Salinari, D. Mattia, F. Babiloni, F. Cincotti: P300-based brain-computer interface for environmental control: An asynchronous approach. J. Neural Eng. 8(2), 025025 (2011).Google ScholarCross Ref
- A. Pinegger, J. Faller, S. Halder, S. C. Wriessnegger, G. R. Müller-Putz: Control or non-control state: That is the question! An asynchronous visual P300- Based BCI approach. J. Neural Eng. 12(1), 014001 (2015).Google ScholarCross Ref
- T. Krumpe, C. Walter, W. Rosenstiel, M. Spüler: Asynchronous P300 classification in a reactive brain-computer interface during an outlier detection task. J. Neural Eng. 13(4), 046015 (2016).Google ScholarCross Ref
- C. R. Panicker, S. Puthusserypady, Y. Sun: An Asynchronous P300 BCI With SSVEP-Based Control State Detection. IEEE T. Bio.-Med. Eng. 58(6), 1781-1788 (2011).Google ScholarCross Ref
- J. Ma, Y. Zhang, A. Cichocki, F. Matsuno: A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control. IEEE T. Bio.-Med. Eng. 62(3), 876-889 (2015).Google ScholarCross Ref
- Y. Yu, Y. Liu, E. Yin, J. Jiang, Z. Zhou, D. Hu: An Asynchronous Hybrid Spelling Approach Based on EEG–EOG Signals for Chinese Character Input. IEEE Trans. Neural Syst. Rehabil. Eng. 27(6), 1292-1302 (2019).Google Scholar
- L. Massin, C. Lahuec, F. Seguin, V. Nourrit, J. Tocnaye: Multipurpose Bio-Monitored Integrated Circuit in a Contact Lens Eye-Tracker. Sensors-basel. 22(2), 595 (2022).Google Scholar
- J. Golenia, M. Wenzel, M. Bogojeski, B. Blankertz: Implicit relevance feedback from electroencephalography and eye tracking in image search. J. Neural Eng. 15(2), 026002 (2018).Google ScholarCross Ref
- S. Gerwin, D. J. Mcfarland, H. Thilo, B. Niels, and J. R. Wolpaw: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE T. Bio.-Med. Eng. 51(6), 1034 (2004).Google Scholar
- L. A. Farwell, and E. Donchin: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510-523 (1988).Google ScholarCross Ref
- D. J. Krusienski, E. W. Sellers, F. Cabestaing, S. Bayoudh, D. J. Mcfarland, T. M. Vaughan, and J. R. Wolpaw: A comparison of classification techniques for the P300 Speller. J. Neural Eng. 3(4), 299-305 (2006).Google ScholarCross Ref
- D. J. Krusienski, E. W. Sellers, D. J. McFarland, T. M. Vaughan, and J. R. Wolpaw: Toward enhanced P300 speller performance. J. Neurosci. Meth. 167(1), 15-21 (2008).Google ScholarCross Ref
- Z. Zhou, E. Yin, Y. Liu, J. Jiang, and D. Hu: A novel task-oriented optimal design for P300-based brain-computer interfaces. J. Neural Eng. 11(5), 056003 (2014).Google ScholarCross Ref
- Y. Yu, Z. Zhou, E. Yin, J. Jiang, Y. Liu, and D. Hu: A P300-based brain-computer interface for Chinese character input. Int. J. Hum-Comput. Int. 32(11), 878-884 (2016).Google ScholarCross Ref
- J. W. Minett, H. Y. Zheng, M. C. M. Fong, L. Zhou, G. Peng, and W. S. Y. Wang: A Chinese text input brain–computer interface based on the P300 speller. Int. J. Hum.-Comput. Int. 28(7), 472-483 (2012).Google ScholarCross Ref
Index Terms
- A practical hybrid BCI speller for Chinese Character Input: Integrating an Eye Tracker into a P300-Based Spelling approach
Recommendations
A novel hybrid BCI speller based on RSVP and SSVEP paradigm
Highlights- A new hybrid paradigm is proposed in this paper for spelling characters by brain signals; this paradigm is a RSVP speller, which is combined with SSVEP ...
Abstract Background and objectiveSteady-state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) are useful methods in the brain-computer interface (BCI) systems. Hybrid BCI systems that combine these two ...
Online detection of p300 and error potentials in a BCI speller
Special issue on processing of brain signals by using hemodynamic and neuroelectromagnetic modalitiesError potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We ...
Hybrid SSVEP/P300 BCI Keyboard
BIOSTEC 2016: Proceedings of the International Joint Conference on Biomedical Engineering Systems and TechnologiesThis paper presents a two stage Brain Computer Interface (BCI) keyboard system that consumes Electroencephalography (EEG) signals based on two evoked potential detection methods: P300 and Steady-State Visual Evoked Potential (SSVEP). In order to develop ...
Comments