A TrAdaBoost-based method for detecting multiple subjects’ P300 potentials_Journal of Biomedical Engineering

Authors：

XU Guizhi ^1,2 , LIN Fang ^1,2 , GONG Minghong ^1,2 ,  LI Mengfan ^1,2 , YU Hongli ^1,2

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, School of Electrical Engineering, Hebei University of Technology, Tianjin 300132, P.R.China;

Corresponding?author：

LI Mengfan, Email: mfli@hebut.edu.cn

Keywords：

brain-computer interface; P300; transfer learning; TrAdaBoost; linear discriminant analysis classifier; support vector machine

DOI：

10.7507/1001-5515.201811025

Video：

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Individual differences of P300 potentials lead to that a large amount of training data must be collected to construct pattern recognition models in P300-based brain-computer interface system, which may cause subjects’ fatigue and degrade the system performance. TrAdaBoost is a method that transfers the knowledge from source area to target area, which improves learning effect in the target area. Our research purposed a TrAdaBoost-based linear discriminant analysis and a TrAdaBoost-based support vector machine to recognize the P300 potentials across multiple subjects. This method first trains two kinds of classifiers separately by using the data deriving from a small amount of data from same subject and a large amount of data from different subjects. Then it combines all the classifiers with different weights. Compared with traditional training methods that use only a small amount of data from same subject or mixed different subjects’ data to directly train, our algorithm improved the accuracies by 19.56% and 22.25% respectively, and improved the information transfer rate of 14.69 bits/min and 15.76 bits/min respectively. The results indicate that the TrAdaBoost-based method has the potential to enhance the generalization ability of brain-computer interface on the individual differences.

Citation： XU Guizhi, LIN Fang, GONG Minghong, LI Mengfan, YU Hongli. A TrAdaBoost-based method for detecting multiple subjects’ P300 potentials. Journal of Biomedical Engineering, 2019, 36(4): 531-540. doi: 10.7507/1001-5515.201811025 Copy

1.	Zhang Yu, Zhou Guoxu, Jin Jing, et al. Sparse Bayesian classification of EEG for brain-computer interface. IEEE Trans Neural Netw Learn Syst, 2016, 27(11): 2256-2267.
2.	Pfurtscheller G, Aranibar A. Event-related cortical desynchronization detected by power measurements of scalp EEG. Electroencephalogr Clin Neurophysiol, 1977, 42(6): 817-826.
3.	吳邊, 蘇煜, 張劍慧, 等. 基于P300電位的新型BCI中文輸入虛擬鍵盤系統. 電子學報, 2009, 37(8): 1733-1738, 1745.
4.	馬忠偉, 高上凱. 基于P300電位的腦機接口系統中參數優化問題的研究. 中國生物醫學工程學報, 2009, 28(6): 851-855.
5.	馬振武, 穆俊林. 2型糖尿病患者的負性情緒及其P300電位的對照研究. 中國康復醫學雜志, 2004, 19(3): 218.
6.	劉聰, 徐曉東, 戴好運, 等. 基于N-back認知任務的正常腦老化事件相關電位分析. 生物醫學工程學雜志, 2017, 34(6): 824-830.
7.	尚淑怡, 尤春景. 認知電位P300的應用及研究進展. 中國康復, 2008, 23(2): 133-135.
8.	范曉麗, 趙朝義, 羅虹, 等. 基于2-back任務下ERP特征的腦力疲勞客觀評價研究. 生物醫學工程學雜志, 2018, 35(6): 15-22.
9.	鄒可, 孫元鋒, 唐向東, 等. 阻塞性睡眠呼吸暫停低通氣綜合征患者早期認知功能損害的事件相關電位研究. 生物醫學工程學雜志, 2014, 31(4): 870-874.
10.	Farwell L A, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol, 1988, 70(6): 510-523.
11.	Tang Jingsheng, Liu Yadong, Hu Dewen, et al. Towards BCI-actuated smart wheelchair system. Biomed Eng Online, 2018, 17: 111-132.
12.	Zhang Zhijun, Huang Yongqian, Chen Siyuan, et al. An intention-driven semi-autonomous intelligent robotic system for drinking. Front Neurorobot, 2017, 11. DOI: 10.3389/fnbot.2017.00048.
13.	王金甲, 楊成杰, 胡備. P300腦機接口控制智能小車系統的設計與實現. 生物醫學工程學雜志, 2013, 30(2): 223-228.
14.	王金甲, 楊成杰. P300腦機接口控制智能家居系統研究. 生物醫學工程學雜志, 2014, 31(4): 762-766.
15.	Oskoei M A, Hu Huosheng. Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng, 2008, 55(8): 1956-1965.
16.	Li Wei, Li Mengfan, Zhou Huihui, et al. A dual stimuli approach combined with convolutional neural network to improve information transfer rate of event-related potential-based brain-computer interface. Int J Neural Syst, 2018, 28(10). DOI: 10.1142/S012906571850034X.
17.	Medrano P, Nyhus E, Smolen A, et al. Individual differences in EEG correlates of recognition memory due to DAT polymorphisms. Brain Behav, 2017, 7(12): e00870.
18.	Jayaram V, Alamgir M, Altun Y, et al. Transfer learning in brain-computer interfaces. IEEE Comput Intell Mag, 2016, 11(1): 20-31.
19.	Lotte F, Congedo M, Lécuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng, 2007, 4(2): R1-R13.
20.	Shin H C, Roth H R, Gao Mingchen, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging, 2016, 35(5): 1285-1298.
21.	Yuan Zhixiang, Bao Damang, Chen Zekai, et al. Integrated transfer learning algorithm using multi-source TrAdaBoost for unbalanced samples classification// International Conference on Computing Intelligence and Information System. Nanjing: IEEE Computer Society, 2017: 188-195.
22.	胡偉, 陳煒峰, 胡凱, 等. 基于改進加權多源TrAdaBoost算法的無參考圖像質量評價方法. 科學技術與工程, 2018, 18(18): 87-93.
23.	謝星宇, 張穎璐. 基于改進的 TrAdaboost 算法的學生成績排名預測. 計算機與現代化, 2016(2): 122-126.
24.	Wei Chunshu, Lin Yuanpin, Wang Yute, et al. Selective transfer learning for EEG-based drowsiness detection// IEEE International Conference on Systems, Man, and Cybernetics. Hong Kong: IEEE, 2016: 3229-3232.
25.	馬忠偉, 高上凱. 基于P300的腦-機接口: 視覺刺激強度對性能的影響. 清華大學學報: 自然科學版, 2008, 48(3): 415-418.
26.	徐桂芝, 王寧, 張天恒, 等. 虛擬現實視覺體驗對事件相關電位影響的研究. 信號處理, 2018, 34(8): 952-962.
27.	Hong Bo, Guo Fei, Liu Tao, et al. N200-speller using motion-onset visual response. Clin Neurophysiol, 2009, 120(9): 1658-1666.
28.	Dai W, Yang Q, Xue G R, et al. Boosting for transfer learning// International Conference on Machine Learning. Oregon: ACM, 2007: 193-200.
29.	付榮榮, 侯培國, 李曼迪. 基于Fisher準則的單次運動想象腦電信號意圖識別研究. 生物醫學工程學雜志, 2018, 35(5): 774-778.
30.	Chen S, Liu J, Zhou Z H. Making FLDA applicable to face recognition with one sample per person. Pattern Recognit, 2004, 37(7): 1553-1555.
31.	Heddam S, Kisi O. Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol, 2018, 559: 499-509.
32.	孫即祥. 現代模式識別. 第2版. 北京: 高等教育出版社, 2008.
33.	Mcfarland D J, Sarnacki W A, Wolpaw J R. Brain-computer interface (BCI) operation: optimizing information transfer rates. Biol Psychol, 2003, 63(3): 237-251.
34.	楊立才, 李金亮, 姚玉翠, 等. 基于F-score特征選擇和支持向量機的P300識別算法. 生物醫學工程學雜志, 2008, 25(1): 23-26, 52.
35.	郁洪強, 趙欣, 汪曣, 等. 過度使用互聯網對事件相關電位N400的影響. 生物醫學工程學雜志, 2008, 25(5): 1014-1020.
36.	Waytowich N R, Lawhern V J, Bohannon A W, et al. Spectral transfer learning using information geometry for a user-independent brain-computer interface. Front Neurosci, 2016, 10: 430.
37.	Adair J, Brownlee A, Daolio F, et al. Evolving training sets for improved transfer learning in brain computer interfaces// Nicosia G, Pardalos P, Giuffrida G, et al. Machine Learning, Optimization, and Big Data. Cham: Springer, 2018.
38.	Islam R, Tanaka T, Molla K I. Multiband tangent space mapping and feature selection for classification of EEG during motor imagery. J Neural Eng, 2018, 15(4). DOI: 10.1088/1741-2552/aac313.
39.	袁鵬. 跨腦信息挖掘及其在腦—機接口中的應用. 北京: 清華大學, 2015.
40.	Wang Yijun, Jung T P. A collaborative brain-computer interface for improving human performance. PLoS One, 2011, 6(5): e20422.

1. Zhang Yu, Zhou Guoxu, Jin Jing, et al. Sparse Bayesian classification of EEG for brain-computer interface. IEEE Trans Neural Netw Learn Syst, 2016, 27(11): 2256-2267.
2. Pfurtscheller G, Aranibar A. Event-related cortical desynchronization detected by power measurements of scalp EEG. Electroencephalogr Clin Neurophysiol, 1977, 42(6): 817-826.
3. 吳邊, 蘇煜, 張劍慧, 等. 基于P300電位的新型BCI中文輸入虛擬鍵盤系統. 電子學報, 2009, 37(8): 1733-1738, 1745.
4. 馬忠偉, 高上凱. 基于P300電位的腦機接口系統中參數優化問題的研究. 中國生物醫學工程學報, 2009, 28(6): 851-855.
5. 馬振武, 穆俊林. 2型糖尿病患者的負性情緒及其P300電位的對照研究. 中國康復醫學雜志, 2004, 19(3): 218.
6. 劉聰, 徐曉東, 戴好運, 等. 基于N-back認知任務的正常腦老化事件相關電位分析. 生物醫學工程學雜志, 2017, 34(6): 824-830.
7. 尚淑怡, 尤春景. 認知電位P300的應用及研究進展. 中國康復, 2008, 23(2): 133-135.
8. 范曉麗, 趙朝義, 羅虹, 等. 基于2-back任務下ERP特征的腦力疲勞客觀評價研究. 生物醫學工程學雜志, 2018, 35(6): 15-22.
9. 鄒可, 孫元鋒, 唐向東, 等. 阻塞性睡眠呼吸暫停低通氣綜合征患者早期認知功能損害的事件相關電位研究. 生物醫學工程學雜志, 2014, 31(4): 870-874.
10. Farwell L A, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol, 1988, 70(6): 510-523.
11. Tang Jingsheng, Liu Yadong, Hu Dewen, et al. Towards BCI-actuated smart wheelchair system. Biomed Eng Online, 2018, 17: 111-132.
12. Zhang Zhijun, Huang Yongqian, Chen Siyuan, et al. An intention-driven semi-autonomous intelligent robotic system for drinking. Front Neurorobot, 2017, 11. DOI: 10.3389/fnbot.2017.00048.
13. 王金甲, 楊成杰, 胡備. P300腦機接口控制智能小車系統的設計與實現. 生物醫學工程學雜志, 2013, 30(2): 223-228.
14. 王金甲, 楊成杰. P300腦機接口控制智能家居系統研究. 生物醫學工程學雜志, 2014, 31(4): 762-766.
15. Oskoei M A, Hu Huosheng. Support vector machine-based classification scheme for myoelectric control applied to upper limb. IEEE Trans Biomed Eng, 2008, 55(8): 1956-1965.
16. Li Wei, Li Mengfan, Zhou Huihui, et al. A dual stimuli approach combined with convolutional neural network to improve information transfer rate of event-related potential-based brain-computer interface. Int J Neural Syst, 2018, 28(10). DOI: 10.1142/S012906571850034X.
17. Medrano P, Nyhus E, Smolen A, et al. Individual differences in EEG correlates of recognition memory due to DAT polymorphisms. Brain Behav, 2017, 7(12): e00870.
18. Jayaram V, Alamgir M, Altun Y, et al. Transfer learning in brain-computer interfaces. IEEE Comput Intell Mag, 2016, 11(1): 20-31.
19. Lotte F, Congedo M, Lécuyer A, et al. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng, 2007, 4(2): R1-R13.
20. Shin H C, Roth H R, Gao Mingchen, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging, 2016, 35(5): 1285-1298.
21. Yuan Zhixiang, Bao Damang, Chen Zekai, et al. Integrated transfer learning algorithm using multi-source TrAdaBoost for unbalanced samples classification// International Conference on Computing Intelligence and Information System. Nanjing: IEEE Computer Society, 2017: 188-195.
22. 胡偉, 陳煒峰, 胡凱, 等. 基于改進加權多源TrAdaBoost算法的無參考圖像質量評價方法. 科學技術與工程, 2018, 18(18): 87-93.
23. 謝星宇, 張穎璐. 基于改進的 TrAdaboost 算法的學生成績排名預測. 計算機與現代化, 2016(2): 122-126.
24. Wei Chunshu, Lin Yuanpin, Wang Yute, et al. Selective transfer learning for EEG-based drowsiness detection// IEEE International Conference on Systems, Man, and Cybernetics. Hong Kong: IEEE, 2016: 3229-3232.
25. 馬忠偉, 高上凱. 基于P300的腦-機接口: 視覺刺激強度對性能的影響. 清華大學學報: 自然科學版, 2008, 48(3): 415-418.
26. 徐桂芝, 王寧, 張天恒, 等. 虛擬現實視覺體驗對事件相關電位影響的研究. 信號處理, 2018, 34(8): 952-962.
27. Hong Bo, Guo Fei, Liu Tao, et al. N200-speller using motion-onset visual response. Clin Neurophysiol, 2009, 120(9): 1658-1666.
28. Dai W, Yang Q, Xue G R, et al. Boosting for transfer learning// International Conference on Machine Learning. Oregon: ACM, 2007: 193-200.
29. 付榮榮, 侯培國, 李曼迪. 基于Fisher準則的單次運動想象腦電信號意圖識別研究. 生物醫學工程學雜志, 2018, 35(5): 774-778.
30. Chen S, Liu J, Zhou Z H. Making FLDA applicable to face recognition with one sample per person. Pattern Recognit, 2004, 37(7): 1553-1555.
31. Heddam S, Kisi O. Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol, 2018, 559: 499-509.
32. 孫即祥. 現代模式識別. 第2版. 北京: 高等教育出版社, 2008.
33. Mcfarland D J, Sarnacki W A, Wolpaw J R. Brain-computer interface (BCI) operation: optimizing information transfer rates. Biol Psychol, 2003, 63(3): 237-251.
34. 楊立才, 李金亮, 姚玉翠, 等. 基于F-score特征選擇和支持向量機的P300識別算法. 生物醫學工程學雜志, 2008, 25(1): 23-26, 52.
35. 郁洪強, 趙欣, 汪曣, 等. 過度使用互聯網對事件相關電位N400的影響. 生物醫學工程學雜志, 2008, 25(5): 1014-1020.
36. Waytowich N R, Lawhern V J, Bohannon A W, et al. Spectral transfer learning using information geometry for a user-independent brain-computer interface. Front Neurosci, 2016, 10: 430.
37. Adair J, Brownlee A, Daolio F, et al. Evolving training sets for improved transfer learning in brain computer interfaces// Nicosia G, Pardalos P, Giuffrida G, et al. Machine Learning, Optimization, and Big Data. Cham: Springer, 2018.
38. Islam R, Tanaka T, Molla K I. Multiband tangent space mapping and feature selection for classification of EEG during motor imagery. J Neural Eng, 2018, 15(4). DOI: 10.1088/1741-2552/aac313.
39. 袁鵬. 跨腦信息挖掘及其在腦—機接口中的應用. 北京: 清華大學, 2015.
40. Wang Yijun, Jung T P. A collaborative brain-computer interface for improving human performance. PLoS One, 2011, 6(5): e20422.

Journal of Biomedical Engineering

A TrAdaBoost-based method for detecting multiple subjects’ P300 potentials

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