Research progress about brain-computer interface technology based on cognitive brain areas and its applications in rehabilitation_Journal of Biomedical Engineering

Authors：

ZHOU Huilin ^1,2 , XU Jialin ¹ , SHI Changcheng ¹ ,  ZUO Guokun ¹

1. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, Zhejiang 315201, P.R.China;
2. University of Chinese Academy of Sciences, Beijing 100049, P.R.China;

Corresponding author：

Keywords：

brain-computer interface; cognitive brain signals; single brain area; multiple hybrid brain areas; rehabilitation medicine

DOI：

10.7507/1001-5515.201711013

Video：

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Brain–computer interface (BCI) technology enable humans to interact with external devices by decoding their brain signals. Despite it has made some significant breakthroughs in recent years, there are still many obstacles in its applications and extensions. The current used BCI control signals are generally derived from the brain areas involved in primary sensory- or motor-related processing. However, these signals only reflect a limited range of limb movement intention. Therefore, additional sources of brain signals for controlling BCI systems need to be explored. Brain signals derived from the cognitive brain areas are more intuitive and effective. These signals can be used for expand the brain signal sources as a new approach. This paper reviewed the research status of cognitive BCI based on the single brain area and multiple hybrid brain areas, and summarized its applications in the rehabilitation medicine. It’s believed that cognitive BCI technologies would become a possible breakthrough for future BCI rehabilitation applications.

Citation： ZHOU Huilin, XU Jialin, SHI Changcheng, ZUO Guokun. Research progress about brain-computer interface technology based on cognitive brain areas and its applications in rehabilitation. Journal of Biomedical Engineering, 2018, 35(5): 799-804. doi: 10.7507/1001-5515.201711013 Copy

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2.	Frolov A A, Húsek D, Biryukova E V, et al. Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain-computer interface based on motor imagery. Neural Network World, 2017, 27(1): 107-137.
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4.	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.
5.	Pattnaik P K, Sarraf J. Brain Computer Interface issues on hand movement. Journal of King Saud University–Computer and Information Sciences, 2018, 30(1): 18-24.
6.	明东, 肖晓琳, 汤佳贝, 等. 基于异步并行诱发策略的混合范式脑-机接口技术. 纳米技术与精密工程, 2015, 13(5): 333-338.
7.	Min B K, Chavarriaga R, Millán J D R. Harnessing prefrontal cognitive signals for brain-machine interfaces. Trends Biotechnol, 2017, 35(7): 585-597.
8.	Yuan Han, He Bin. Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng, 2014, 61(5): 1425-1435.
9.	Yi Weibo, Qiu Shuang, Qi Hongzhi, et al. EEG feature comparison and classification of simple and compound limb motor imagery. J Neuroeng Rehabil, 2013, 10(1): 106.
10.	Yi Weibo, Qiu Shuang, Wang Kun, et al. EEG oscillatory patterns and classification of sequential compound limb motor imagery. J Neuroeng Rehabil, 2016, 13(1): 11.
11.	Ang K K, Guan C. EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(4): 392-401.
12.	Aricó P, Borghini G, Di Flumeri G A, et al. Passive BCI in operational environments: insights, recent advances, and future trends. IEEE Trans Biomed Eng, 2017, 64(7): 1431-1436.
13.	Sánchez A M, Gaume A, Dreyfus G, et al. A cognitive brain-computer interface prototype for the continuous monitoring of visual working memory load. IEEE International Workshop on Machine Learning for Signal Processing, 2015: 7324370.
14.	Iturrate I, Chavarriaga R, Montesano L, et al. Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control. Scientific Reports, 2015, 5: 13893.
15.	Khan M J, Hong K S. Hybrid EEG-fNIRS-based eight-command decoding for BCI: application to quadcopter control. Frontiers in Neurorobotics, 2017, 11: 6.
16.	Hauschild M, Mulliken G H, Fineman I, et al. Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands. Proc Natl Acad Sci U S A, 2012, 109(42): 17075-17080.
17.	Aflalo T, Kellis S, Klaes C, et al. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science, 2015, 348(6237): 906-910.
18.	Ryun S, Kim J S, Lee S H, et al. Movement type prediction before its onset using signals from prefrontal area: an electrocorticography study. Biomed Research International, 2014, 2014(8): 783203.
19.	Zhang Z, Jiao X, Jiang J, et al. Passive BCI based on sustained attention detection: an fNIRS study//Liu C L, Hussain A, Luo B, et al. International Conference on Brain Inspired Cognitive Systems: Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science. Cham: Springer, 2016, 10023: 220-227.
20.	Shin J, Müller K R, Hwang H J. Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic. Scientific Reports, 2016, 6: 36203.
21.	Khan M J, Hong M J, Hong K S. Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface. Front Hum Neurosci, 2014, 8(1): 244.
22.	Hong K S, Naseer N, Kim Y H. Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI. Neurosci Lett, 2015, 587: 87-92.
23.	Naseer N, Hong K S. Decoding answers to four-choice questions using functional near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 2015, 23(1): 23-31.
24.	Aziz F, Arof H, Mokhtar N, et al. HMM based automated wheelchair navigation using EOG traces in EEG. J Neural Eng, 2014, 11(5): 056018.
25.	Chen Lanlan, Zhao Yu, Zhang Jian, et al. Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst Appl, 2015, 42(21): 7344-7355.
26.	Lew E Y L, Chavarriaga R, Silvoni S, et al. Single trial prediction of self-paced reaching directions from EEG signals. Front Neurosci, 2014, 8(1): 222.
27.	Jayaram V, Hohmann M, Just J, et al. Task-induced frequency modulation features for brain-computer interfacing. J Neural Eng, 2017, 14(5): 056015.
28.	Hong K S, Khan M J. Hybrid brain-computer interface techniques for improved classification accuracy and increased number of commands: a review. Frontiers in Neurorobotics, 2017, 11: 35.
29.	Naseer N, Hong K S. fNIRS-based brain-computer interfaces: a review. Front Hum Neurosci, 2015, 9: 3.
30.	Sood M, Besson P, Muthalib M, et al. NIRS-EEG joint imaging during transcranial direct current stimulation: Online parameter estimation with an autoregressive model. J Neurosci Methods, 2016, 274: 71-80.
31.	Guhathakurta D, Dutta A. Computational pipeline for NIRS-EEG joint imaging of tDCS-evoked cerebral responses-an application in ischemic stroke. Frontiers in Neuroscience, 2016, 10: 261.
32.	Fomina T, Schölkopf B, Grosse-Wentrup M. Towards cognitive brain-computer interfaces for patients with amyotrophic lateral sclerosis//Proceedings of 7th Computer Science and Electronic Engineering Conference. New York: IEEE, 2015: 77-80. DOI: 10.1109/CEEC.2015.7332703.
33.	Fomina T, Lohmann G, Erb M, et al. Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS. J Neural Eng, 2016, 13(6): 066021.
34.	Hohmann M R, Fomina T, Jayaram V, et al. A cognitive brain-computer interface for patients with amyotrophic lateral sclerosis. Prog Brain Res, 2016, 228: 221-239.
35.	刘亚男, 陈元园, 沙淼, 等. 静息态功能连接网络对大脑不同水平认知衰退的研究进展. 生物医学工程学杂志, 2017, 34(4): 632-636.
36.	Anguera J A, Boccanfuso J, Rintoul J L, et al. Video game training enhances cognitive control in older adults. Nature, 2013, 501(7465): 97-101.
37.	Edelman B J, Baxter B, He Bin. EEG source imaging enhances the decoding of complex Right-Hand motor imagery tasks. IEEE Trans Biomed Eng, 2016, 63(1, SI): 4-14.

1. Leeb R, Tonin L, Rohm M, et al. Towards independence: a BCI telepresence robot for people with severe motor disabilities. Proceedings of the IEEE, 2015, 103(6, SI): 969-982.
2. Frolov A A, Húsek D, Biryukova E V, et al. Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain-computer interface based on motor imagery. Neural Network World, 2017, 27(1): 107-137.
3. He Bin, Baxter B, Edelman B J, et al. Noninvasive brain-computer interfaces based on sensorimotor rhythms. Proceedings of the IEEE, 2015, 103(6, SI): 907-925.
4. 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.
5. Pattnaik P K, Sarraf J. Brain Computer Interface issues on hand movement. Journal of King Saud University–Computer and Information Sciences, 2018, 30(1): 18-24.
6. 明东, 肖晓琳, 汤佳贝, 等. 基于异步并行诱发策略的混合范式脑-机接口技术. 纳米技术与精密工程, 2015, 13(5): 333-338.
7. Min B K, Chavarriaga R, Millán J D R. Harnessing prefrontal cognitive signals for brain-machine interfaces. Trends Biotechnol, 2017, 35(7): 585-597.
8. Yuan Han, He Bin. Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng, 2014, 61(5): 1425-1435.
9. Yi Weibo, Qiu Shuang, Qi Hongzhi, et al. EEG feature comparison and classification of simple and compound limb motor imagery. J Neuroeng Rehabil, 2013, 10(1): 106.
10. Yi Weibo, Qiu Shuang, Wang Kun, et al. EEG oscillatory patterns and classification of sequential compound limb motor imagery. J Neuroeng Rehabil, 2016, 13(1): 11.
11. Ang K K, Guan C. EEG-based strategies to detect motor imagery for control and rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(4): 392-401.
12. Aricó P, Borghini G, Di Flumeri G A, et al. Passive BCI in operational environments: insights, recent advances, and future trends. IEEE Trans Biomed Eng, 2017, 64(7): 1431-1436.
13. Sánchez A M, Gaume A, Dreyfus G, et al. A cognitive brain-computer interface prototype for the continuous monitoring of visual working memory load. IEEE International Workshop on Machine Learning for Signal Processing, 2015: 7324370.
14. Iturrate I, Chavarriaga R, Montesano L, et al. Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control. Scientific Reports, 2015, 5: 13893.
15. Khan M J, Hong K S. Hybrid EEG-fNIRS-based eight-command decoding for BCI: application to quadcopter control. Frontiers in Neurorobotics, 2017, 11: 6.
16. Hauschild M, Mulliken G H, Fineman I, et al. Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands. Proc Natl Acad Sci U S A, 2012, 109(42): 17075-17080.
17. Aflalo T, Kellis S, Klaes C, et al. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science, 2015, 348(6237): 906-910.
18. Ryun S, Kim J S, Lee S H, et al. Movement type prediction before its onset using signals from prefrontal area: an electrocorticography study. Biomed Research International, 2014, 2014(8): 783203.
19. Zhang Z, Jiao X, Jiang J, et al. Passive BCI based on sustained attention detection: an fNIRS study//Liu C L, Hussain A, Luo B, et al. International Conference on Brain Inspired Cognitive Systems: Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science. Cham: Springer, 2016, 10023: 220-227.
20. Shin J, Müller K R, Hwang H J. Near-infrared spectroscopy (NIRS)-based eyes-closed brain-computer interface (BCI) using prefrontal cortex activation due to mental arithmetic. Scientific Reports, 2016, 6: 36203.
21. Khan M J, Hong M J, Hong K S. Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface. Front Hum Neurosci, 2014, 8(1): 244.
22. Hong K S, Naseer N, Kim Y H. Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI. Neurosci Lett, 2015, 587: 87-92.
23. Naseer N, Hong K S. Decoding answers to four-choice questions using functional near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 2015, 23(1): 23-31.
24. Aziz F, Arof H, Mokhtar N, et al. HMM based automated wheelchair navigation using EOG traces in EEG. J Neural Eng, 2014, 11(5): 056018.
25. Chen Lanlan, Zhao Yu, Zhang Jian, et al. Automatic detection of alertness/drowsiness from physiological signals using wavelet-based nonlinear features and machine learning. Expert Syst Appl, 2015, 42(21): 7344-7355.
26. Lew E Y L, Chavarriaga R, Silvoni S, et al. Single trial prediction of self-paced reaching directions from EEG signals. Front Neurosci, 2014, 8(1): 222.
27. Jayaram V, Hohmann M, Just J, et al. Task-induced frequency modulation features for brain-computer interfacing. J Neural Eng, 2017, 14(5): 056015.
28. Hong K S, Khan M J. Hybrid brain-computer interface techniques for improved classification accuracy and increased number of commands: a review. Frontiers in Neurorobotics, 2017, 11: 35.
29. Naseer N, Hong K S. fNIRS-based brain-computer interfaces: a review. Front Hum Neurosci, 2015, 9: 3.
30. Sood M, Besson P, Muthalib M, et al. NIRS-EEG joint imaging during transcranial direct current stimulation: Online parameter estimation with an autoregressive model. J Neurosci Methods, 2016, 274: 71-80.
31. Guhathakurta D, Dutta A. Computational pipeline for NIRS-EEG joint imaging of tDCS-evoked cerebral responses-an application in ischemic stroke. Frontiers in Neuroscience, 2016, 10: 261.
32. Fomina T, Schölkopf B, Grosse-Wentrup M. Towards cognitive brain-computer interfaces for patients with amyotrophic lateral sclerosis//Proceedings of 7th Computer Science and Electronic Engineering Conference. New York: IEEE, 2015: 77-80. DOI: 10.1109/CEEC.2015.7332703.
33. Fomina T, Lohmann G, Erb M, et al. Self-regulation of brain rhythms in the precuneus: a novel BCI paradigm for patients with ALS. J Neural Eng, 2016, 13(6): 066021.
34. Hohmann M R, Fomina T, Jayaram V, et al. A cognitive brain-computer interface for patients with amyotrophic lateral sclerosis. Prog Brain Res, 2016, 228: 221-239.
35. 刘亚男, 陈元园, 沙淼, 等. 静息态功能连接网络对大脑不同水平认知衰退的研究进展. 生物医学工程学杂志, 2017, 34(4): 632-636.
36. Anguera J A, Boccanfuso J, Rintoul J L, et al. Video game training enhances cognitive control in older adults. Nature, 2013, 501(7465): 97-101.
37. Edelman B J, Baxter B, He Bin. EEG source imaging enhances the decoding of complex Right-Hand motor imagery tasks. IEEE Trans Biomed Eng, 2016, 63(1, SI): 4-14.

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