Key technologies for intelligent brain-computer interaction based on magnetoencephalography_Journal of Biomedical Engineering

Authors：

XU Haotian ^1,2 , GONG Anmin ³ , DING Peng ^1,2 , LUO Jiangong ^1,2 , CHEN Chao ⁴ ,  FU Yunfa ^1,2

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China;
2. Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China;
3. School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an 710000, P. R. China;
4. Tianjin University of Technology School of Electrical and Electronic Engineering, Tianjin 300000, P. R. China;

Corresponding author：

Keywords：

Magnetoencephalography; Brain-computer interface; Intelligent brain-computer interface; Magnetoencephalography-brain-computer interface experimental paradigm design; Magnetoencephalography feature extraction.

DOI：

10.7507/1001-5515.202108069

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Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.

Citation： XU Haotian, GONG Anmin, DING Peng, LUO Jiangong, CHEN Chao, FU Yunfa. Key technologies for intelligent brain-computer interaction based on magnetoencephalography. Journal of Biomedical Engineering, 2022, 39(1): 198-206. doi: 10.7507/1001-5515.202108069 Copy

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2.	Graimann B, Allison B, Pfurtscheller G. Brain-computer interfaces: revolutionizing human-computer interaction. Springer Publishing Company, 2013.
3.	Ramsey N F, Millán J D R. Brain-computer interfaces. Elsevier, 2020.
4.	伏云发, 丁鹏, 罗建功, 等. 脑-计算机接口. 北京: 国防工业出版社, 2022.
5.	Cetin O, Temurtas F. A comparative study on classification of magnetoencephalography signals using probabilistic neural network and multilayer neural network. Soft Comput, 2021, 25(3): 2267-2275.
6.	Ovchinnikova A O, Vasilyev A N, Zubarev I P, et al. MEG-based detection of voluntary eye fixations used to control a computer. Front Neurosci, 2021, 15: 619591.
7.	Roy S, Rathee D, Chowdhury A, et al. Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data. J Neural Eng, 2020, 17(5): 056037.
8.	Chholak P, Niso G, Maksimenko V A, et al. Visual and kinesthetic modes affect motor imagery classification in untrained subjects. Sci Rep, 2019, 9(1): 9838.
9.	Rathee D, Raza H, Roy S, et al. A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface. Sci Data, 2021, 8(1): 120.
10.	Belkacem A N. Real-time human-like robot control based on brain-computer interface//2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), IEEE, 2021: 9378751.
11.	Feng Yulong, Xiao Wei, Wu Teng, et al. A new recognition method for the auditory evoked magnetic fields. Comput Intell Neurosci, 2021, 2021: 6645270.
12.	伏云发, 龚安民, 陈超, 等. 面向实用的脑-机接口:缩小研究与实际应用之间的差距. 北京: 科学出版社, 2022.
13.	吕晓彤, 丁鹏, 李思语, 等. 脑机接口人因工程及应用:以人为中心的脑机接口设计和评价方法. 生物医学工程学杂志, 2021, 38(2): 210-223.
14.	伏云发, 杨秋红, 徐宝磊, 等. 脑-机接口原理与实践. 北京: 国防工业出版社, 2017.
15.	Chholak P, Kurkin S A, Hramov A E, et al. Event-related coherence in visual cortex and brain noise: an MEG study. Applied Sciences, 2021, 11(1): 375.
16.	Gaur P, Kaushik G, Pachori R B, et al. Comparison analysis: single and multichannel EMD-based filtering with application to BCI//Machine Intelligence And Signal Analysis. Singapore: Springer,2019, 2019: 107-118.
17.	Niso G, Tadel F, Bock E, et al. Brainstorm pipeline analysis of resting-state data from the open MEG archive. Front Neurosci, 2019, 13: 284.
18.	Shahid A , Wahab M , Rafiuddin N , et al. Decrypting wrist movement from MEG signal using SVM classifier. Journal of Intelligent and Fuzzy Systems, 2018, 35: 1-8.
19.	Tao Y, Yan N, Wang G. Hand movement prediction based on EEG signals by combining MEMD and CSP//2nd International Conference on Image Processing and Machine Vision, 2020: 105-112.
20.	Feng J K, Jin J, Daly I, et al. An optimized channel selection method based on multifrequency CSP-rank for motor imagery-based BCI system. Comput Intell Neurosci, 2019, 2019(6): 8068357.
21.	Roffo G, Melzi S, Castellani U, et al. Infinite feature selection: a graph-based feature filtering approach. IEEE Trans Pattern Anal Mach Intell, 2021, 43(12): 4396-4410.
22.	Kim J, Kim M Y, Kwon H, et al. Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography. J Neurosci Methods, 2020, 338: 108688.
23.	Goni M R, Rahman T. Predictive modeling on MEG signal to classify hand and wrist movement using UNEQ and KNN//2020 IEEE Region 10 Symposium (TENSYMP), IEEE, 2020: 815-818.
24.	Babajani-Feremi A, Noorizadeh N, Mudigoudar B, et al. Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology. Neuroimage Clin, 2018, 19: 990-999.
25.	Caliskan A, Yuksel M E, Badem H, et al. A deep neural network classifier for decoding human brain activity based on magnetoencephalography. Elektronika Ir Elektrotechnika, 2017, 23(2): 63-67.
26.	Zubarev I, Zetter R, Halme H L, et al. Adaptive neural network classifier for decoding MEG signals. Neuroimage, 2019, 197: 425-434.
27.	Dash D, Ferrari P, Heitzman D, et al. Decoding speech from single trial MEG signals using convolutional neural networks and transfer learning//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019: 5531-5535.
28.	Collura T F. 神经反馈原理与实践, 伏云发, 龚安民, 南文雅, 译. 北京:电子工业出版社,2021.
29.	Rana K D, Khan S, Hämäläinen M S, et al. A computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attention. Biomed Eng Online, 2020, 19(1): 45.
30.	Grigorev N A, Savosenkov A O, Lukoyanov M V, et al. A BCI-based vibrotactile neurofeedback training improves motor cortical excitability during motor imagery. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1583-1592.
31.	Mladenović J. Standardization of protocol design for user training in EEG-based brain-computer interface. J Neural Eng, 2021, 18(1): 011003.
32.	Foldes S T, Boninger M L, Weber D J, et al. Effects of MEG-based neurofeedback for hand rehabilitation after tetraplegia: preliminary findings in cortical modulations and grip strength. J Neural Eng, 2020, 17(2): 026019.
33.	Bagherzadeh Y, Baldauf D, Pantazis D, et al. Alpha synchrony and the neurofeedback control of spatial attention. Neuron, 2020, 105(3): 577-587.
34.	李红卫, 陈小刚. 基于高级控制策略的脑-机接口控制机械臂系统. 北京生物医学工程, 2019, 38(1): 36-41.
35.	Fahimi F, Zhang Z, Goh W B, et al. Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI. J Neural Eng, 2019, 16(2): 026007.
36.	Zhang X, Yao L, Wang X, et al. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng, 2020, 18(3): 031002.
37.	Abdellaoui I A, Fernandez J G, Sahinli C, et al. Deep brain state classification of MEG data. arXiv preprint, arXiv:2007.00897, 2020.
38.	戴廷飞, 刘邈, 叶阳阳, 等. 人机共享控制机器人系统的应用与发展. 仪器仪表学报, 2019(3): 62-73.
39.	Millan J J D R, Galan F, Vanhooydonck D, et al. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. Annu Int Conf IEEE Eng Med Biol Soc, 2009, 2009: 3361-3364.
40.	Hsieh Y W, Lee M T, Lin Y H, et al. Motor cortical activity during observing a video of real hand movements versus computer graphic hand movements: an MEG study. Brain Sci, 2020, 11(1): 6.
41.	Roberts G, Holmes N, Alexander N, et al. Towards OPM-MEG in a virtual reality environment. Neuroimage, 2019, 199: 408-417.
42.	Boto E, Holmes N, Leggett J, et al. Moving magnetoencephalography towards real-world applications with a wearable system. Nature, 2018, 555(7698): 657-661.
43.	Sorbo A R, Lombardi G, La Brocca L, et al. Unshielded magnetocardiography: repeatability and reproducibility of automatically estimated ventricular repolarization parameters in 204 healthy subjects. Ann Noninvasive Electrocardiol, 2018, 23(3): e12526.

1. 伏云发, 郭衍龙, 张夏冰, 等. 脑-机接口——革命性的人机交互. 北京: 国防工业出版社, 2020.
2. Graimann B, Allison B, Pfurtscheller G. Brain-computer interfaces: revolutionizing human-computer interaction. Springer Publishing Company, 2013.
3. Ramsey N F, Millán J D R. Brain-computer interfaces. Elsevier, 2020.
4. 伏云发, 丁鹏, 罗建功, 等. 脑-计算机接口. 北京: 国防工业出版社, 2022.
5. Cetin O, Temurtas F. A comparative study on classification of magnetoencephalography signals using probabilistic neural network and multilayer neural network. Soft Comput, 2021, 25(3): 2267-2275.
6. Ovchinnikova A O, Vasilyev A N, Zubarev I P, et al. MEG-based detection of voluntary eye fixations used to control a computer. Front Neurosci, 2021, 15: 619591.
7. Roy S, Rathee D, Chowdhury A, et al. Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data. J Neural Eng, 2020, 17(5): 056037.
8. Chholak P, Niso G, Maksimenko V A, et al. Visual and kinesthetic modes affect motor imagery classification in untrained subjects. Sci Rep, 2019, 9(1): 9838.
9. Rathee D, Raza H, Roy S, et al. A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface. Sci Data, 2021, 8(1): 120.
10. Belkacem A N. Real-time human-like robot control based on brain-computer interface//2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), IEEE, 2021: 9378751.
11. Feng Yulong, Xiao Wei, Wu Teng, et al. A new recognition method for the auditory evoked magnetic fields. Comput Intell Neurosci, 2021, 2021: 6645270.
12. 伏云发, 龚安民, 陈超, 等. 面向实用的脑-机接口:缩小研究与实际应用之间的差距. 北京: 科学出版社, 2022.
13. 吕晓彤, 丁鹏, 李思语, 等. 脑机接口人因工程及应用:以人为中心的脑机接口设计和评价方法. 生物医学工程学杂志, 2021, 38(2): 210-223.
14. 伏云发, 杨秋红, 徐宝磊, 等. 脑-机接口原理与实践. 北京: 国防工业出版社, 2017.
15. Chholak P, Kurkin S A, Hramov A E, et al. Event-related coherence in visual cortex and brain noise: an MEG study. Applied Sciences, 2021, 11(1): 375.
16. Gaur P, Kaushik G, Pachori R B, et al. Comparison analysis: single and multichannel EMD-based filtering with application to BCI//Machine Intelligence And Signal Analysis. Singapore: Springer,2019, 2019: 107-118.
17. Niso G, Tadel F, Bock E, et al. Brainstorm pipeline analysis of resting-state data from the open MEG archive. Front Neurosci, 2019, 13: 284.
18. Shahid A , Wahab M , Rafiuddin N , et al. Decrypting wrist movement from MEG signal using SVM classifier. Journal of Intelligent and Fuzzy Systems, 2018, 35: 1-8.
19. Tao Y, Yan N, Wang G. Hand movement prediction based on EEG signals by combining MEMD and CSP//2nd International Conference on Image Processing and Machine Vision, 2020: 105-112.
20. Feng J K, Jin J, Daly I, et al. An optimized channel selection method based on multifrequency CSP-rank for motor imagery-based BCI system. Comput Intell Neurosci, 2019, 2019(6): 8068357.
21. Roffo G, Melzi S, Castellani U, et al. Infinite feature selection: a graph-based feature filtering approach. IEEE Trans Pattern Anal Mach Intell, 2021, 43(12): 4396-4410.
22. Kim J, Kim M Y, Kwon H, et al. Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography. J Neurosci Methods, 2020, 338: 108688.
23. Goni M R, Rahman T. Predictive modeling on MEG signal to classify hand and wrist movement using UNEQ and KNN//2020 IEEE Region 10 Symposium (TENSYMP), IEEE, 2020: 815-818.
24. Babajani-Feremi A, Noorizadeh N, Mudigoudar B, et al. Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology. Neuroimage Clin, 2018, 19: 990-999.
25. Caliskan A, Yuksel M E, Badem H, et al. A deep neural network classifier for decoding human brain activity based on magnetoencephalography. Elektronika Ir Elektrotechnika, 2017, 23(2): 63-67.
26. Zubarev I, Zetter R, Halme H L, et al. Adaptive neural network classifier for decoding MEG signals. Neuroimage, 2019, 197: 425-434.
27. Dash D, Ferrari P, Heitzman D, et al. Decoding speech from single trial MEG signals using convolutional neural networks and transfer learning//2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019: 5531-5535.
28. Collura T F. 神经反馈原理与实践, 伏云发, 龚安民, 南文雅, 译. 北京:电子工业出版社,2021.
29. Rana K D, Khan S, Hämäläinen M S, et al. A computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attention. Biomed Eng Online, 2020, 19(1): 45.
30. Grigorev N A, Savosenkov A O, Lukoyanov M V, et al. A BCI-based vibrotactile neurofeedback training improves motor cortical excitability during motor imagery. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1583-1592.
31. Mladenović J. Standardization of protocol design for user training in EEG-based brain-computer interface. J Neural Eng, 2021, 18(1): 011003.
32. Foldes S T, Boninger M L, Weber D J, et al. Effects of MEG-based neurofeedback for hand rehabilitation after tetraplegia: preliminary findings in cortical modulations and grip strength. J Neural Eng, 2020, 17(2): 026019.
33. Bagherzadeh Y, Baldauf D, Pantazis D, et al. Alpha synchrony and the neurofeedback control of spatial attention. Neuron, 2020, 105(3): 577-587.
34. 李红卫, 陈小刚. 基于高级控制策略的脑-机接口控制机械臂系统. 北京生物医学工程, 2019, 38(1): 36-41.
35. Fahimi F, Zhang Z, Goh W B, et al. Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI. J Neural Eng, 2019, 16(2): 026007.
36. Zhang X, Yao L, Wang X, et al. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. J Neural Eng, 2020, 18(3): 031002.
37. Abdellaoui I A, Fernandez J G, Sahinli C, et al. Deep brain state classification of MEG data. arXiv preprint, arXiv:2007.00897, 2020.
38. 戴廷飞, 刘邈, 叶阳阳, 等. 人机共享控制机器人系统的应用与发展. 仪器仪表学报, 2019(3): 62-73.
39. Millan J J D R, Galan F, Vanhooydonck D, et al. Asynchronous non-invasive brain-actuated control of an intelligent wheelchair. Annu Int Conf IEEE Eng Med Biol Soc, 2009, 2009: 3361-3364.
40. Hsieh Y W, Lee M T, Lin Y H, et al. Motor cortical activity during observing a video of real hand movements versus computer graphic hand movements: an MEG study. Brain Sci, 2020, 11(1): 6.
41. Roberts G, Holmes N, Alexander N, et al. Towards OPM-MEG in a virtual reality environment. Neuroimage, 2019, 199: 408-417.
42. Boto E, Holmes N, Leggett J, et al. Moving magnetoencephalography towards real-world applications with a wearable system. Nature, 2018, 555(7698): 657-661.
43. Sorbo A R, Lombardi G, La Brocca L, et al. Unshielded magnetocardiography: repeatability and reproducibility of automatically estimated ventricular repolarization parameters in 204 healthy subjects. Ann Noninvasive Electrocardiol, 2018, 23(3): e12526.

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Key technologies for intelligent brain-computer interaction based on magnetoencephalography

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