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Medical & Biological Engineering & Computing
Erschienen in: Medical & Biological Engineering & Computing 3/2021

16.02.2021 | Original Article

EEG-based hybrid QWERTY mental speller with high information transfer rate

verfasst von: Er. Akshay Katyal, Rajesh Singla

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 3/2021

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Abstract

Background

Brain-computer interface (BCI) spellers detect variations in brain waves to help subjects communicate with the world. This study introduces a P300-SSVEP hybrid BCI-based QWERTY speller.

Methods

The proposed hybrid speller, combines SSVEP and P300 features using a hybrid paradigm. P300 was used as time division multiplexing index which results in the use of lesser number of assumed frequencies for SSVEP elicitation. Each flickering frequency was also assigned a unique colour, to enhance system accuracy.

Results

On the basis of 20 subjects, an average accuracy of classification of 96.42% and a mean information transfer rate (ITR) of 131.0 bits per min. (BPM) was achieved during the free spelling trial (trial-F).

Comparison

The t test results revealed that the hybrid QWERTY speller performed significantly better (on the basis of mean classification accuracy and ITR) as compared to the traditional P300 speller) and the QWERTY SSVEP speller. Also, the amount of time taken to spell a word was significantly lesser in the case of hybrid QWERTY speller in contrast to traditional P300 speller while it was almost the same as compared to QWERTY SSVEP speller.

Conclusion

QWERTY speller outperformed the stereotypical P300 speller as well as QWERTY SSVEP speller.

Graphical abstract

Vorheriger Artikel Classification of orthostatic intolerance through data analytics
Nächster Artikel Applicability of a single camera-based catheter navigation system using teeth arch as an anatomical landmark for superselective intraarterial infusion in advanced oral cancer treatment

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Literatur
1.
Dornhege G, Millán JR , Hinterberger T, McFarland DJ, Muller KU (2007) Toward brain-computer interfacing. Vol. 63. MIT Press, Cambridge, MA, pp 32–33
2.
Baillet S, Mosher JC, Leahy RM (2001) Electromagnetic brain mapping. IEEE Signal Process Mag 18:14–30
3.
Waldert S, Pistohl T, Braun C et al (2009) A review on directional information in neural signals for brain-machine interfaces. J Physiol 103:244–254
4.
Christopher de Charms R, Christoff K, Glover GH et al (2004) Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21:436–443
5.
Coyle SM, Ward TE, Markham CM (2007) Brain–computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng 4:219–226PubMed
6.
Shin J, Im C-H (2018) Performance prediction for a near-infrared spectroscopy-brain–computer interface using resting-state functional connectivity of the prefrontal cortex. Int J Neural Syst 28:1850023PubMed
7.
Brigitte R, Elbert T, Lutzenberger W, Birbaumer N (1984) Operant control of slow brain potentials: A tool in the investigation of the potential’s meaning and its relation to attentional dysfunction. In Self-regulation of the brain and behavior, Springer, pp. 227–239
8.
Strehl U, Trevorrow T, Veit R, Hinterberger T, Kotchoubey B, Erb M, Birbaumer N (2006) Deactivation of brain areas during self-regulation of slow cortical potentials in seizure patients. Appl Psychophysiol Biofeedback 31:85–94PubMed
9.
Niedermeyer E (2005) The normal EEG of the waking adult. Electroencephalogr Basic Princ Clin Appl Relat fields 167:155–164
10.
Feng J, Yin E, Jin J, Saab R, Daly I, Wang X, Hu D, Cichocki A (2018) Towards correlation-based time window selection method for motor imagery BCIs. Neural Networks 102:87–95PubMed
11.
Jin J, Miao Y, Daly I, Zuo C, Hu D, Cichocki A (2019) Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Networks 118:262–270PubMed
12.
Squires KC, Donchin E, Herning RI, McCarthy G (1977) On the influence of task relevance and stimulus probability on event-related-potential components. Electroencephalogr Clin Neurophysiol 42:1–14PubMed
13.
Wang Y, Wang R, Gao X, Hong B, Gao S (2006) A practical VEP-based brain-computer interface. IEEE Trans neural Syst Rehabil Eng 14:234–240PubMed
14.
Ahn S, Kim K, Jun SC (2016) Steady-state somatosensory evoked potential for brain-computer interface—present and future. Front Hum Neurosci 9:716PubMedPubMedCentral
15.
Fazel-Rezai R, Allison BZ, Guger C et al (2012) P300 brain computer interface: current challenges and emerging trends. Front Neuroeng 5:14PubMedPubMedCentral
16.
Allison BZ, Brunner C, Kaiser V, Müller-Putz GR, Neuper C, Pfurtscheller G (2010) Toward a hybrid brain–computer interface based on imagined movement and visual attention. J Neural Eng 7:26007PubMed
17.
Jin J, Li S, Daly I, Miao Y, Liu C, Wang X, Cichocki A (2019) The study of generic model set for reducing calibration time in P300-based brain–computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(1):3–12PubMed
18.
Regan D (1989) Human brain electrophysiology: evoked potentials and evoked magnetic fields in science and medicine. Elsevier, The University of Michigan, pp 22–24
19.
Pritchard WS (1981) Psychophysiology of P300. Psychol Bull 89:506–540PubMed
20.
Combaz A, Van Hulle MM (2015) Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface. PLoS One 10:e0121481PubMedPubMedCentral
21.
Müller-Putz GR, Scherer R, Brauneis C, Pfurtscheller G (2005) Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components. J Neural Eng 2:123–130PubMed
22.
Hwang H-J, Lim J-H, Jung Y-J, Choi H, Lee SW, Im CH (2012) Development of an SSVEP-based BCI spelling system adopting a QWERTY-style LED keyboard. J Neurosci Methods 208:59–65PubMed
23.
Trejo LJ, Rosipal R, Matthews B (2006) Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials. IEEE Trans neural Syst Rehabil Eng 14:225–229PubMed
24.
Lalor EC, Kelly SP, Finucane C, Burke R, Smith R, Reilly RB, McDarby G (2005) Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP J Adv Signal Process 2005:706906
25.
26.
Cecotti H, Volosyak I, Gräser A (2010) Reliable visual stimuli on LCD screens for SSVEP based BCI. In: 2010 18th European Signal Processing Conference. IEEE, pp 919–923
27.
Wang Y, Chen X, Gao X, Gao S (2016) A benchmark dataset for SSVEP-based brain–computer interfaces. IEEE Trans Neural Syst Rehabil Eng 25:1746–1752PubMed
28.
29.
Zhang Y, Xu P, Liu T, Hu J, Zhang R, Yao D (2012) Multiple frequencies sequential coding for SSVEP-based brain-computer interface. PLoS One 7:e29519PubMedPubMedCentral
30.
Wolpaw JR, Birbaumer N, McFarland DJ et al (2002) Brain–computer interfaces for communication and control. Clin Neurophysiol 113:767–791PubMed
31.
Hong K-S, Khan MJ (2017) Hybrid brain–computer interface techniques for improved classification accuracy and increased number of commands: a review. Front Neurorobot 11:35PubMedPubMedCentral
32.
Setare A, Rabbi A, Azinfar L, Fazel-Rezai R (2013) A review of P300 SSVEP and hybrid P300/SSVEP brain-computer interface systems. In Dr. Reza Fazel-Rezai (Ed) Recent Progress and Future Prospects, InTech. https://​doi.​org/​10.​5772/​56135
33.
Ma T, Li H, Deng L, Yang H, Lv X, Li P, Li F, Zhang R, Liu T, Yao D, Xu P (2017) The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential. J Neural Eng 14:26015
34.
Ko L-W, Ranga SSK, Komarov O, Chen C-C (2017) Development of single-channel hybrid BCI system using motor imagery and SSVEP. J Healthc Eng 2017:1–7
35.
Lim J-H, Lee J-H, Hwang H-J, Kim DH, Im CH (2015) Development of a hybrid mental spelling system combining SSVEP-based brain–computer interface and webcam-based eye tracking. Biomed Signal Process Control 21:99–104
36.
Yu T, Xiao J, Wang F, Zhang R, Gu Z, Cichocki A, Li Y (2015) Enhanced motor imagery training using a hybrid BCI with feedback. IEEE Trans Biomed Eng 62:1706–1717PubMed
37.
Mouli S, Palaniappan R (2017) Hybrid BCI utilising SSVEP and P300 event markers for reliable and improved classification using LED stimuli. In: 2017 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp 127–131
38.
Chang MH, Lee JS, Heo J, Park KS (2016) Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI. J Neurosci Methods 258:104–113PubMed
39.
Lin Z, Zhang C, Wu W, Gao X (2006) Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs. IEEE Trans Biomed Eng 53:2610–2614PubMed
40.
Wu Y, Li M, Wang J (2016) Toward a hybrid brain-computer interface based on repetitive visual stimuli with missing events. J Neuroeng Rehabil 13:66PubMedPubMedCentral
41.
Lin K, Chen X, Huang X, Ding Q, Gao X (2015) A Hybrid BCI speller based on the combination of EMG envelopes and SSVEP. Applied Informatics 2(1):1–12
42.
Yin E, Jiang J, Yu Y, et al (2013) A subarea-location joint spelling paradigm for the BCI control. In: International Conference on Intelligent Science and Big Data Engineering. Springer, pp 368–375
43.
Edlinger G, Holzner C, Guger C (2011) A hybrid brain-computer interface for smart home control. In: International Conference on Human-Computer Interaction. Springer, pp 417–426
44.
Volosyak I, Valbuena D, Luth T, Gräser A (2010) Towards an ssvep based bci with high itr. IEEE Trans Biomed Eng
45.
Xu M, Han J, Wang Y, Jung TP, Ming D (2020) Implementing over 100 command codes for a high-speed hybrid brain-computer interface using concurrent P300 and SSVEP features. IEEE Trans Biomed Eng 67:3073–3082PubMed
46.
Kundu S, Ari S (2020) P300 based character recognition using convolutional neural network and support vector machine. Biomed Signal Process Control 55:101645
47.
Ma Z, Xie Z, Qiu T, Cheng J (2020) Driving event-related potential-based speller by localized posterior activities: an offline study. Math Biosci Eng 17:789–801
48.
Yu Y, Liu Y, Yin E, Jiang J, Zhou Z, Hu D (2019) An asynchronous hybrid spelling approach based on EEG–EOG signals for Chinese character input. IEEE Trans Neural Syst Rehabil Eng 27:1292–1302PubMed
49.
Liu D, Liu C, Hong B (2019) Bi-directional visual motion based BCI speller. In: 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp 589–592
50.
Podmore JJ, Breckon TP, Aznan NKN, Connolly JD (2019) On the relative contribution of deep convolutional neural networks for SSVEP-based bio-signal decoding in BCI speller applications. IEEE Trans Neural Syst Rehabil Eng 27:611–618PubMed
51.
Xu M, Xiao X, Wang Y, Qi H, Jung TP, Ming D (2018) A brain–computer interface based on miniature-event-related potentials induced by very small lateral visual stimuli. IEEE Trans Biomed Eng 65:1166–1175PubMed
52.
Chen X, Chen Z, Gao S, Gao X (2014) A high-itr ssvep-based bci speller. Brain-Computer Interfaces 1:181–191
53.
Noyes J (1983) The QWERTY keyboard: a review. Int J Man Mach Stud 18:265–281
54.
55.
Bin G, Gao X, Yan Z, Hong B, Gao S (2009) An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method. J Neural Eng 6:46002
56.
Colwell KA, Ryan DB, Throckmorton CS, Sellers EW, Collins LM (2014) Channel selection methods for the P300 Speller. J Neurosci Methods 232:6–15PubMedPubMedCentral
57.
Ille N, Berg P, Scherg M (2002) Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies. J Clin Neurophysiol 19:113–124PubMed
58.
Hoffmann U, Vesin J-M, Ebrahimi T, Diserens K (2008) An efficient P300-based brain–computer interface for disabled subjects. J Neurosci Methods 167:115–125PubMed
59.
Daly I, Billinger M, Laparra-Hernández J, Aloise F, García ML, Faller J, Scherer R, Müller-Putz G (2013) On the control of brain-computer interfaces by users with cerebral palsy. Clin Neurophysiol 124:1787–1797PubMed
60.
Erkan E, Akbaba M (2018) A study on performance increasing in SSVEP based BCI application. Eng Sci Technol an Int J 21:421–427
61.
İşcan Z, Dokur Z (2014) A novel steady-state visually evoked potential-based brain–computer interface design: character plotter. Biomed Signal Process Control 10:145–152
62.
Safi SMM, Pooyan M, Nasrabadi AM (2018) Improving the performance of the SSVEP-based BCI system using optimized singular spectrum analysis (OSSA). Biomed Signal Process Control 46:46–58
63.
Wang M, Daly I, Allison BZ, Jin J, Zhang Y, Chen L, Wang X (2015) A new hybrid BCI paradigm based on P300 and SSVEP. J Neurosci Methods 244:16–25PubMed
64.
Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F (2018) A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng 15:31005
65.
66.
Fletcher T (2009) Support vector machines explained. Tutor Pap
67.
Abdulaal MJ, Casson AJ, Gaydecki P (2018) Performance of nested vs. non-nested SVM cross-validation methods in visual BCI: validation study. In: 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, pp 1680–1684
68.
Santos MS, Abreu PH, Germán R-B, García-Laencina PJ (2018) Improving the classifier performance in motor imagery task classification: what are the steps in the classification process that we should worry about? Int J Comput Intell Syst 11:1278–1293
69.
Billinger M, Daly I, Kaiser V, Jin J (2013) Is it significant? Guidelines for reporting BCI performance towards practical brain–computer interfaces ed BZ Allison, S Dunne, R Leeb, J d R Millán and A Nijholt
Metadaten
Titel
EEG-based hybrid QWERTY mental speller with high information transfer rate
verfasst von
Er. Akshay Katyal
Rajesh Singla
Publikationsdatum
16.02.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Medical & Biological Engineering & Computing / Ausgabe 3/2021
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-020-02310-w

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