Research on performance of motor-imagery-based brain-computer interface in different complexity of Chinese character patterns_Journal of Biomedical Engineering

Authors：

ZUO Cili , MAO Ying , LIU Qianqian , WANG Xingyu ,  JIN Jing

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, P.R. China;

Corresponding author：

JIN Jing, Email: jinjingat@gmail.com

Keywords：

brain-computer interface; motor imagery; sensorimotor rhythm

DOI：

10.7507/1001-5515.202010031

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The traditional paradigm of motor-imagery-based brain-computer interface (BCI) is abstract, which cannot effectively guide users to modulate brain activity, thus limiting the activation degree of the sensorimotor cortex. It was found that the motor imagery task of Chinese characters writing was better accepted by users and helped guide them to modulate their sensorimotor rhythms. However, different Chinese characters have different writing complexity (number of strokes), and the effect of motor imagery tasks of Chinese characters with different writing complexity on the performance of motor-imagery-based BCI is still unclear. In this paper, a total of 12 healthy subjects were recruited for studying the effects of motor imagery tasks of Chinese characters with two different writing complexity (5 and 10 strokes) on the performance of motor-imagery-based BCI. The experimental results showed that, compared with Chinese characters with 5 strokes, motor imagery task of Chinese characters writing with 10 strokes obtained stronger sensorimotor rhythm and better recognition performance (P < 0.05). This study indicated that, appropriately increasing the complexity of the motor imagery task of Chinese characters writing can obtain stronger motor imagery potential and improve the recognition accuracy of motor-imagery-based BCI, which provides a reference for the design of the motor-imagery-based BCI paradigm in the future.

Citation： ZUO Cili, MAO Ying, LIU Qianqian, WANG Xingyu, JIN Jing. Research on performance of motor-imagery-based brain-computer interface in different complexity of Chinese character patterns. Journal of Biomedical Engineering, 2021, 38(3): 417-424, 454. doi: 10.7507/1001-5515.202010031 Copy

1.	Hoffmann U, Vesin J M, Ebrahimi T, et al. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods, 2008, 167(1): 115-125.
2.	Yu Y, Liu Y, Yin E, et al. An asynchronous hybrid spelling approach based on EEG-EOG signals for Chinese character input. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(6): 1292-1302.
3.	Pan J, Xie Q, Qin P, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain, 2020, 143(4): 1177-1189.
4.	Zuo C, Jin J, Yin E, et al. Novel hybrid brain-computer interface system based on motor imagery and P300. Cogn Neurodyn, 2020, 14(2): 253-265.
5.	Jin J, Liu C, Daly I, et al. Bispectrum-based channel selection for motor imagery based brain-computer interfacing. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(10): 2153-2163.
6.	Jin J, Li S, Daly I, et al. The study of generic model set for reducing calibration time in P300-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(1): 3-12.
7.	Jin J, Chen Z, Xu R, et al. Developing a novel tactile P300 brain-computer interface with a cheeks-stim paradigm. IEEE Trans Biomed Eng, 2020, 67(9): 2585-2593.
8.	Pfurtscheller G, Brunner C, Schlögl A, et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage, 2006, 31(1): 153–159.
9.	Qiu Z, Allison B Z, Jin J, et al. Optimized motor imagery paradigm based on imagining Chinese characters writing movement. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(7): 1009-1017.
10.	Xu M, Han J, Wang Y, et al. Implementing over 100 command codes for a high-speed hybrid brain-computer interface using concurrent P300 and SSVEP features. IEEE Trans Biomed Eng, 2020, 67(11): 3073-3082.
11.	Xiao X, Xu M, Jin J, et al. Discriminative canonical pattern matching for single-trial classification of ERP components. IEEE Trans Biomed Eng, 2020, 67(8): 2266-2275.
12.	Li F, Yi C, Liao Y, et al. Reconfiguration of brain network between resting-state and P300 task. IEEE Trans Cogn Dev Syst, 2020. DOI: 10.1109/TCDS.2020.2965135.
13.	Yang Z, Guo D, Zhang Y, et al. Visual evoked response modulation occurs in a complementary manner under dynamic circuit framework. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(10): 2005-2014.
14.	许敏鹏, 魏泽. 基于脑卒中后运动康复领域的运动想象的研究. 生物医学工程学杂志, 2020, 37(1): 169-173.
15.	Byrd E M, Jablonski R J, Vance D E. Understanding anosognosia for hemiplegia after stroke. Rehabil Nurs, 2020, 45(1): 3-15.
16.	Jin J, Xiao R, Daly I, et al. Internal feature selection method of CSP based on L1-Norm and Dempster-Shafer theory. IEEE Trans Neural Netw Learn Syst, 2020, 99: 1-12.
17.	Zuo C, Miao Y, Wang X, et al. Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces. J Neurosci Methods, 2020, 340: 108725.
18.	Cantillo-Negrete J, Carino-Escobar R I, Carrillo-Mora P, et al. Motor imagery-based brain-computer interface coupled to a robotic hand orthosis aimed for neurorehabilitation of stroke patients. J Healthc Eng, 2018, 2018: 1-10.
19.	Yu Y, Liu Y, Jiang J, et al. An asynchronous control paradigm based on sequential motor imagery and its application in wheelchair navigation. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(12): 2367-2375.
20.	Edelman B J, Meng J, Suma D, et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci Robot, 2019, 4(31): eaaw6844.
21.	Carvalho R, Dias N, Cerqueira J J. Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review. Physiother Res Int, 2019, 24(2): e1764.
22.	Pichiorri F, Morone G, Petti M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol, 2015, 77(5): 851-865.
23.	Marins T, Rodrigues E C, Bortolini T, et al. Structural and functional connectivity changes in response to short-term neurofeedback training with motor imagery. Neuroimage, 2019, 194: 283-290.
24.	Blankertz B, Sannelli C, Halder S, et al. Neurophysiological predictor of SMR-based BCI performance. Neuroimage, 2010, 51(4): 1303-1309.
25.	Yu Tianyou, Xiao Jun, Wang Fangyi, et al. Enhanced motor imagery training using a hybrid BCI with feedback. IEEE Trans Biomed Eng, 2015, 62(7): 1706-1717.
26.	Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 2001, 89(7): 1123-1134.
27.	Hwang H J, Kwon K, Im C H. Neurofeedback-based motor imagery training for brain-computer interface (BCI). J Neurosci Methods, 2009, 179(1): 150-156.
28.	Xia B, Zhang Q, Xie H, et al. A neurofeedback training paradigm for motor imagery based brain-computer interface//The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane: QLD, 2012: 1-4.
29.	Vourvopoulos A, Pardo O M, Lefebvre S, et al. Effects of a brain-computer interface with virtual reality (VR) neurofeedback: a pilot study in chronic stroke patients. Front Hum Neurosci, 2019, 13: 210.
30.	Abdalsalam E, Yusoff M Z, Malik A, et al. Modulation of sensorimotor rhythms for brain-computer interface using motor imagery with online feedback. Signal Image Video Process, 2018, 12(3): 557-564.
31.	Neuper C, Scherer R, Reiner M, et al. Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Cogn Brain Res, 2005, 25(3): 668-677.
32.	Gibson R M, Chennu S, Owen A M, et al. Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography. Clin Neurophysiol, 2014, 125(8): 1556-1567.
33.	Park S H, Lee D, Lee S G. Filter bank regularized common spatial pattern ensemble for small sample motor imagery classification. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(2): 498-505.
34.	Qi Feifei, Wu Wei, Yu Zhuliang, et al. Spatiotemporal-filtering-based channel selection for single-trial EEG classification. IEEE Trans Cybern, 2020, 51(2): 558-567.
35.	Feng J, Yin E, Jin J, et al. Towards correlation-based time window selection method for motor imagery BCIs. Neural Netw, 2018, 102: 87-95.
36.	Lotte F, Bougrain L, Cichocki A, et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng, 2018, 15(3): 031005.
37.	Kuhtz-Buschbeck J P, Mahnkopf C, Holzknecht C, et al. Effector-independent representations of simple and complex imagined finger movements: a combined fMRI and TMS study. Eur J Neurosci, 2003, 18(12): 3375-3387.
38.	Holper L, Wolf M. Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study. J Neuroeng Rehabil, 2011, 8(1): 34.
39.	Roosink M, Zijdewind I. Corticospinal excitability during observation and imagery of simple and complex hand tasks: implications for motor rehabilitation. Behav Brain Res, 2010, 213(1): 35-41.
40.	Mulder T. Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm, 2007, 114(10): 1265-1278.

1. Hoffmann U, Vesin J M, Ebrahimi T, et al. An efficient P300-based brain-computer interface for disabled subjects. J Neurosci Methods, 2008, 167(1): 115-125.
2. Yu Y, Liu Y, Yin E, et al. An asynchronous hybrid spelling approach based on EEG-EOG signals for Chinese character input. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(6): 1292-1302.
3. Pan J, Xie Q, Qin P, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain, 2020, 143(4): 1177-1189.
4. Zuo C, Jin J, Yin E, et al. Novel hybrid brain-computer interface system based on motor imagery and P300. Cogn Neurodyn, 2020, 14(2): 253-265.
5. Jin J, Liu C, Daly I, et al. Bispectrum-based channel selection for motor imagery based brain-computer interfacing. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(10): 2153-2163.
6. Jin J, Li S, Daly I, et al. The study of generic model set for reducing calibration time in P300-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng, 2020, 28(1): 3-12.
7. Jin J, Chen Z, Xu R, et al. Developing a novel tactile P300 brain-computer interface with a cheeks-stim paradigm. IEEE Trans Biomed Eng, 2020, 67(9): 2585-2593.
8. Pfurtscheller G, Brunner C, Schlögl A, et al. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage, 2006, 31(1): 153–159.
9. Qiu Z, Allison B Z, Jin J, et al. Optimized motor imagery paradigm based on imagining Chinese characters writing movement. IEEE Trans Neural Syst Rehabil Eng, 2017, 25(7): 1009-1017.
10. Xu M, Han J, Wang Y, et al. Implementing over 100 command codes for a high-speed hybrid brain-computer interface using concurrent P300 and SSVEP features. IEEE Trans Biomed Eng, 2020, 67(11): 3073-3082.
11. Xiao X, Xu M, Jin J, et al. Discriminative canonical pattern matching for single-trial classification of ERP components. IEEE Trans Biomed Eng, 2020, 67(8): 2266-2275.
12. Li F, Yi C, Liao Y, et al. Reconfiguration of brain network between resting-state and P300 task. IEEE Trans Cogn Dev Syst, 2020. DOI: 10.1109/TCDS.2020.2965135.
13. Yang Z, Guo D, Zhang Y, et al. Visual evoked response modulation occurs in a complementary manner under dynamic circuit framework. IEEE Trans Neural Syst Rehabil Eng, 2019, 27(10): 2005-2014.
14. 许敏鹏, 魏泽. 基于脑卒中后运动康复领域的运动想象的研究. 生物医学工程学杂志, 2020, 37(1): 169-173.
15. Byrd E M, Jablonski R J, Vance D E. Understanding anosognosia for hemiplegia after stroke. Rehabil Nurs, 2020, 45(1): 3-15.
16. Jin J, Xiao R, Daly I, et al. Internal feature selection method of CSP based on L1-Norm and Dempster-Shafer theory. IEEE Trans Neural Netw Learn Syst, 2020, 99: 1-12.
17. Zuo C, Miao Y, Wang X, et al. Temporal frequency joint sparse optimization and fuzzy fusion for motor imagery-based brain-computer interfaces. J Neurosci Methods, 2020, 340: 108725.
18. Cantillo-Negrete J, Carino-Escobar R I, Carrillo-Mora P, et al. Motor imagery-based brain-computer interface coupled to a robotic hand orthosis aimed for neurorehabilitation of stroke patients. J Healthc Eng, 2018, 2018: 1-10.
19. Yu Y, Liu Y, Jiang J, et al. An asynchronous control paradigm based on sequential motor imagery and its application in wheelchair navigation. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(12): 2367-2375.
20. Edelman B J, Meng J, Suma D, et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci Robot, 2019, 4(31): eaaw6844.
21. Carvalho R, Dias N, Cerqueira J J. Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review. Physiother Res Int, 2019, 24(2): e1764.
22. Pichiorri F, Morone G, Petti M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol, 2015, 77(5): 851-865.
23. Marins T, Rodrigues E C, Bortolini T, et al. Structural and functional connectivity changes in response to short-term neurofeedback training with motor imagery. Neuroimage, 2019, 194: 283-290.
24. Blankertz B, Sannelli C, Halder S, et al. Neurophysiological predictor of SMR-based BCI performance. Neuroimage, 2010, 51(4): 1303-1309.
25. Yu Tianyou, Xiao Jun, Wang Fangyi, et al. Enhanced motor imagery training using a hybrid BCI with feedback. IEEE Trans Biomed Eng, 2015, 62(7): 1706-1717.
26. Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 2001, 89(7): 1123-1134.
27. Hwang H J, Kwon K, Im C H. Neurofeedback-based motor imagery training for brain-computer interface (BCI). J Neurosci Methods, 2009, 179(1): 150-156.
28. Xia B, Zhang Q, Xie H, et al. A neurofeedback training paradigm for motor imagery based brain-computer interface//The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane: QLD, 2012: 1-4.
29. Vourvopoulos A, Pardo O M, Lefebvre S, et al. Effects of a brain-computer interface with virtual reality (VR) neurofeedback: a pilot study in chronic stroke patients. Front Hum Neurosci, 2019, 13: 210.
30. Abdalsalam E, Yusoff M Z, Malik A, et al. Modulation of sensorimotor rhythms for brain-computer interface using motor imagery with online feedback. Signal Image Video Process, 2018, 12(3): 557-564.
31. Neuper C, Scherer R, Reiner M, et al. Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Cogn Brain Res, 2005, 25(3): 668-677.
32. Gibson R M, Chennu S, Owen A M, et al. Complexity and familiarity enhance single-trial detectability of imagined movements with electroencephalography. Clin Neurophysiol, 2014, 125(8): 1556-1567.
33. Park S H, Lee D, Lee S G. Filter bank regularized common spatial pattern ensemble for small sample motor imagery classification. IEEE Trans Neural Syst Rehabil Eng, 2018, 26(2): 498-505.
34. Qi Feifei, Wu Wei, Yu Zhuliang, et al. Spatiotemporal-filtering-based channel selection for single-trial EEG classification. IEEE Trans Cybern, 2020, 51(2): 558-567.
35. Feng J, Yin E, Jin J, et al. Towards correlation-based time window selection method for motor imagery BCIs. Neural Netw, 2018, 102: 87-95.
36. Lotte F, Bougrain L, Cichocki A, et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng, 2018, 15(3): 031005.
37. Kuhtz-Buschbeck J P, Mahnkopf C, Holzknecht C, et al. Effector-independent representations of simple and complex imagined finger movements: a combined fMRI and TMS study. Eur J Neurosci, 2003, 18(12): 3375-3387.
38. Holper L, Wolf M. Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study. J Neuroeng Rehabil, 2011, 8(1): 34.
39. Roosink M, Zijdewind I. Corticospinal excitability during observation and imagery of simple and complex hand tasks: implications for motor rehabilitation. Behav Brain Res, 2010, 213(1): 35-41.
40. Mulder T. Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm, 2007, 114(10): 1265-1278.

Journal of Biomedical Engineering

Research on performance of motor-imagery-based brain-computer interface in different complexity of Chinese character patterns

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content