應用鏡像神經元訓練搭配黎曼空間幾何之想像運動模型.

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李泓漳(Hung-Chang Lee) 查詢紙本館藏

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電機工程學系

論文名稱

應用鏡像神經元訓練搭配黎曼空間幾何之想像運動模型.
(Classification of Multiclass Motor Imagery EEG based on mirror neuron Using Riemannian Geometry Methods)

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至系統瀏覽論文 (2026-8-4以後開放)

摘要(中)

腦機介面(Brain Computer Interface, BCI)作為殘疾人士與現實世界進行溝通的橋樑。想像運動(Motor Imagery)是BCI架構中一個非常重要的分支，提供大腦與機器之間一種控制方法。其中想像運動與動作觀察的鏡像運動皆被認為是學習運動的有效工具。在複雜的協調任務學習中，動作觀察(Action Observation, AO)比想像運動能夠更有效的達到大腦訓練的效果。本論文以鏡像神經元系統(human mirror neuron system, hMNS)作為訓練想像運動的方法，讓受試者有一個視覺化的動作參考對象，結合虛擬實境探討左右手不同角度的動作。實驗分成兩部份，第一部份以動作觀察激發鏡像神經元系統，第二部分將人物換成箭頭，以箭頭指示想像運動，最後使用鏡像運動的腦波資料模型預測想像運動的腦波資料，此二階段資料量測的目的在於嘗試建立一個利用鏡像神經元訓練想像運動的新方法。實驗中將乾式腦波電極設置在10-20 EEG System之Fp1、Fp2、F3、Fz、F4、C3、Cz、C4、P3、Pz、P4、O1、O2的位置，以動作後兩秒的腦波資料，經過重疊頻帶濾波器組分成10個子頻帶，並計算協方差矩陣投射到黎曼空間中，使用黎曼均值搭配切線空間投影法將資料轉到歐氏空間中進行預測及分類。結果顯示經過鏡像學習的受試者在想像運動上的表現有明顯的提升，能夠分類細微動作的差異。未來有望提升BCI在各領域的應用。

摘要(英)

Brain Computer Interface(BCI) is provided as a bridge for disabled people to communicate with the world. Motor imagery(MI) is an important part of the BCI. MI and action observation (AO) have been considered as effective ways for motor learning. In complex learning tasks, AO is considered a more effective method to train brain motor cortex, compared to MI. In our study, we constructed a human mirror neuron system (hMNS) as pre-trained task for suject’s MI training. The hMNS provided subjects reference images for MI, and tried to guide the movements of the left/right hand in different angles under virtual reality(VR) environment. Our EEG experiment contained two parts. In the first part, subjects were requested to performed a hMNS task by viewing AO videos, and EEG data were collected to train a pre-trained model for the subsequent MI task. In the second part, MI task was given to subjects and the MI classification was performed using the pre-trained hMNS deep learning model obtained from the experiment of the first part. In the MI task, instead of view hand motions, an arrow indicator was used to indicate the direction for MI. The purpose of the aforementioned two-step EEG experiment is trying to build a new MI training process based on a hMNS pre-training approach. A thirteen-channel dry-electrode wireless EEG system was used to measure EEG signals from electrode positions at Fp1, Fp2, F3, Fz, F4, C3, Cz, C4, P3, Pz, P4, O1, and O2 according to the international 10-20 montage system. The EEG data were real-time filtered into ten frequency bands as features. The covariance matrixes obtained from the features of ten frequency bands were calculated and projected into the Riemann space. The mean of the projected values in Riemann were calculated and the tangent space mapping(TSM) method was used to transfer EEG data to the Euclidean space for prediction and classification. The results showed that the subjects, who participated in pre-trained by hMNS task, had significant improvements in the following MI tasks. In the future, it is expected to enhance the application of BCI in various fields.

關鍵字(中)

★ 腦電波
★ 腦機介面
★ 想像運動
★ 鏡像神經元
★ 黎曼幾何

關鍵字(英)

★ Motor Imagery
★ Brain Computer Interface
★ Mirror neuron, Riemannian

論文目次

目錄
第一章緒論 1
1-1 前言 1
1-2 研究動機與目的 2
1-3 文獻回顧與探討 3
1-4 論文章節架構 5
第二章原理介紹 6
2-1 腦機介面 6
2-2 腦電訊號 7
2-2-1 常用腦電訊號基本類型 7
2-2-2 大腦皮質區域及功能 8
2-2-3 腦電波測量方法與位置 9
2-2-4 腦波機硬體架構 11
2-3 腦電波分析方法 13
2-3-1 巴特沃斯濾波器 13
2-3-2 事件相關去同步化腦波與事件相關同步化腦波 14
2-3-3 小波變換 15
2-4 鏡像神經元 16
2-5 黎曼幾何與黎曼流形基礎 18
2-5-1 腦電圖訊號之黎曼幾何特性 19
2-5-2 黎曼距離 20
2-5-3 黎曼對數/指數透影 21
2-5-4 黎曼均值 23
2-5-5 黎曼切線空間投影 24
2-6 特徵選擇 27
2-7 模型評估介紹 28
第三章研究設計與方法 29
3-1系統架構 29
3-1-1實驗環境系統架構 29
3-1-2 鏡像學習實驗介紹 30
3-1-3 想像運動實驗介紹 31
3-2 腦波資料處理及模型架構 33
3-2-1 腦波資料處理 33
3-2-2 模型架構 36
3-2-3 重疊頻帶之帶通濾波器組 37
3-2-4 輸入資料型態 38
3-2-5 鏡像運動實驗對象 39
第四章實驗結果與討論 40
4-1 小波轉換的時頻圖 40
4-2 不同腦電極通道數量的比較 46
4-3 重疊濾波器組不同頻帶的數量 47
4-4重疊濾波器組頻寬大小與重疊頻寬大小 48
4-5 黎曼幾何中的歐氏距離與黎曼距離 49
4-5-1 黎曼均值對應不同類別之間的距離 49
4-5-2 黎曼空間中不同類別之間的距離 51
4-6 特徵選取之實驗結果 54
4-7 多類別鏡像運動腦電圖訊號之實驗結果 58
4-8 鏡像運動學習分類之混淆矩陣 61
4-9 鏡像模型預測想像運動 66
4-9-1 鏡像模型預測不同時間長度的想像運動 67
4-9-2 不同模型預測想像運動資料 68
4-10 鏡像學習不同階段時期的探討 69
4-11對於鏡像的討論 70
第五章結論與未來展望 72
第六章參考文獻 73

參考文獻

1. Pfurtscheller, G. and C. Neuper, Motor imagery and direct brain-computer communication. Proceedings of the IEEE, 2001. 89(7): p. 1123-1134.
2. Barachant, A., et al. Riemannian geometry applied to BCI classification. in International conference on latent variable analysis and signal separation. 2010. Springer.
3. Singh, A., S. Lal, and H.W. Guesgen, Reduce calibration time in motor imagery using spatially regularized symmetric positives-definite matrices based classification. Sensors, 2019. 19(2): p. 379.
4. Avaznia, C., et al. Breast cancer classification using covariance description in Riemannian geometry. in 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP). 2017. IEEE.
5. Graimann, B., B. Allison, and G. Pfurtscheller, A gentle introduction to brain—computer interface (BCI) systems. Brain–Computer Interfaces: Revolutionizing Human–Computer Interaction, ed. by B. Graimann, BZ Allison, G. Pfurtscheller (Springer Verlag, Berlin Heidelberg, 2010), 2010: p. 1-28.
6. Koles, Z.J., The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalography and clinical Neurophysiology, 1991. 79(6): p. 440-447.
7. Ramoser, H., J. Muller-Gerking, and G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE transactions on rehabilitation engineering, 2000. 8(4): p. 441-446.
8. Xu, B., et al., Wavelet transform time-frequency image and convolutional network-based motor imagery EEG classification. IEEE Access, 2018. 7: p. 6084-6093.
9. Graimann, B., B.Z. Allison, and G. Pfurtscheller, Brain-computer interfaces: Revolutionizing human-computer interaction. 2010: Springer Science & Business Media.
10. Lance, B.J., et al., Brain–computer interface technologies in the coming decades. Proceedings of the IEEE, 2012. 100(Special Centennial Issue): p. 1585-1599.
11. Guan, C., M. Thulasidas, and J. Wu. High performance P300 speller for brain-computer interface. in IEEE International Workshop on Biomedical Circuits and Systems, 2004. 2004. IEEE.
12. Schwartz, A.B., et al., Brain-controlled interfaces: movement restoration with neural prosthetics. Neuron, 2006. 52(1): p. 205-220.
13. Käthner, I., et al., Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain–computer interface. Biological psychology, 2014. 102: p. 118-129.
14. Abdulkader, S.N., A. Atia, and M.-S.M. Mostafa, Brain computer interfacing: Applications and challenges. Egyptian Informatics Journal, 2015. 16(2): p. 213-230.
15. Shih, J.J., D.J. Krusienski, and J.R. Wolpaw. Brain-computer interfaces in medicine. in Mayo Clinic Proceedings. 2012. Elsevier.
16. Tanji, K., et al., Functional significance of the electrocorticographic auditory responses in the premotor cortex. Frontiers in neuroscience, 2015. 9: p. 78.
17. Guger, C., et al., How many people are able to operate an EEG-based brain-computer interface (BCI)? IEEE transactions on neural systems and rehabilitation engineering, 2003. 11(2): p. 145-147.
18. Blankertz, B., et al., Neurophysiological predictor of SMR-based BCI performance. Neuroimage, 2010. 51(4): p. 1303-1309.
19. Jiang, A., et al., Efficient CSP Algorithm With Spatio-Temporal Filtering for Motor Imagery Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020. 28(4): p. 1006-1016.
20. Bhattacharyya, S., et al. Performance analysis of LDA, QDA and KNN algorithms in left-right limb movement classification from EEG data. in 2010 international conference on systems in medicine and biology. 2010. IEEE.
21. LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436-444.
22. Sutskever, I., O. Vinyals, and Q.V. Le, Sequence to sequence learning with neural networks. arXiv preprint arXiv:1409.3215, 2014.
23. Li, J., et al., Feature learning from incomplete EEG with denoising autoencoder. Neurocomputing, 2015. 165: p. 23-31.
24. Antoniades, A., et al. Deep learning for epileptic intracranial EEG data. in 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). 2016. IEEE.
25. Islam, M.R., et al. Classification of motor imagery BCI using multiband tangent space mapping. in 2017 22nd International Conference on Digital Signal Processing (DSP). 2017. IEEE.
26. Yang, P., et al., MLP With Riemannian Covariance for Motor Imagery Based EEG Analysis. IEEE Access, 2020. 8: p. 139974-139982.
27. Wu, F., et al., A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery. Frontiers in Human Neuroscience, 2021. 15: p. 104.
28. Vaid, S., P. Singh, and C. Kaur. EEG signal analysis for BCI interface: A review. in 2015 fifth international conference on advanced computing & communication technologies. 2015. IEEE.
29. Pfurtscheller, G., Functional brain imaging based on ERD/ERS. Vision research, 2001. 41(10-11): p. 1257-1260.
30. Pfurtscheller, G., Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. Electroencephalography and clinical neurophysiology, 1992. 83(1): p. 62-69.
31. Koshino, Y. and E. Niedermeyer, Enhancement of rolandic mu-rhythm by pattern vision. Electroencephalography and Clinical Neurophysiology, 1975. 38(5): p. 535-538.
32. Fox, N.A., et al., Assessing human mirror activity with EEG mu rhythm: A meta-analysis. Psychological bulletin, 2016. 142(3): p. 291.
33. Newman-Norlund, R.D., et al., The mirror neuron system is more active during complementary compared with imitative action. Nature neuroscience, 2007. 10(7): p. 817-818.
34. Filimon, F., et al., Human cortical representations for reaching: mirror neurons for execution, observation, and imagery. Neuroimage, 2007. 37(4): p. 1315-1328.
35. Franceschini, M., et al., Clinical relevance of action observation in upper-limb stroke rehabilitation: a possible role in recovery of functional dexterity. A randomized clinical trial. Neurorehabilitation and neural repair, 2012. 26(5): p. 456-462.
36. Gonzalez-Rosa, J.J., et al., Action observation and motor imagery in performance of complex movements: Evidence from EEG and kinematics analysis. Behavioural Brain Research, 2015. 281: p. 290-300.
37. Agnew, Z.K., R.J. Wise, and R. Leech, Dissociating object directed and non-object directed action in the human mirror system; implications for theories of motor simulation. PloS one, 2012. 7(4): p. e32517.
38. Summerfield, Q., Lipreading and audio-visual speech perception. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 1992. 335(1273): p. 71-78.
39. Silsbee, P.L. and A.C. Bovik, Computer lipreading for improved accuracy in automatic speech recognition. IEEE Transactions on Speech and Audio Processing, 1996. 4(5): p. 337-351.
40. Gatti, R., et al., The effect of action observation/execution on mirror neuron system recruitment: an fMRI study in healthy individuals. Brain imaging and behavior, 2017. 11(2): p. 565-576.
41. Luo, T.-j., et al., Effect of different movement speed modes on human action observation: an EEG study. Frontiers in neuroscience, 2018. 12: p. 219.
42. Lupu, R.G., et al., BCI and FES based therapy for stroke rehabilitation using VR facilities. Wireless Communications and Mobile Computing, 2018. 2018.
43. Kakadiaris, I. and J. Kybic. Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. 2004. Springer.
44. Eaves, D.L., L. Behmer Jr, and S. Vogt, EEG and behavioural correlates of different forms of motor imagery during action observation in rhythmical actions. Brain and cognition, 2016. 106: p. 90-103.
45. Ulloa, E.R. and J.A. Pineda, Recognition of point-light biological motion: mu rhythms and mirror neuron activity. Behavioural brain research, 2007. 183(2): p. 188-194.
46. Neuper, C., et al., Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clinical neurophysiology, 2009. 120(2): p. 239-247.
47. Ono, T., A. Kimura, and J. Ushiba, Daily training with realistic visual feedback improves reproducibility of event-related desynchronisation following hand motor imagery. Clinical Neurophysiology, 2013. 124(9): p. 1779-1786.
48. Nagai, H. and T. Tanaka, Action observation of own hand movement enhances event-related desynchronization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019. 27(7): p. 1407-1415.
49. Meier, B., N. Rothen, and S. Walter, Developmental aspects of synaesthesia across the adult lifespan. Frontiers in human neuroscience, 2014. 8: p. 129.
50. Galléa, C., et al., Error processing during online motor control depends on the response accuracy. Behavioural brain research, 2008. 193(1): p. 117-125.
51. Taube, W., et al., Non-physical practice improves task performance in an unstable, perturbed environment: motor imagery and observational balance training. Frontiers in human neuroscience, 2014. 8: p. 972.

指導教授

李柏磊

審核日期

2021-8-16

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