在本論文中我們提出一種結合了腦波電位和啟發式演算法的協同控制策略，開發一套系統讓受測者能利用腦波進行在二維平面的迷宮遊戲。 腦機介面應用最主要的問題之一是腦電波分類準確度不高，一般而言我們很難去定義所有的腦波種類。為了讓人們利用自己的意志去精準的操作移動方向，近年有來許多關於協同控制策略的研究成果發表，這些研究方法透過結合腦波發出的訊號和預先設計好的執行命令，讓系統的執行結果能夠更接近使用者真正的選擇。我們設計一套協同控制系統，此系統中有一球體在迷宮中移動，每一次球體移動後受測者觀察移動方向並且評估球體是否正確往終點前進，受測者在評估時產生的腦波會被記錄下來，作為回饋傳回協同控制系統，系統根據受測者不同的評估結果和我們提出的啟發式演算法決定球體下一步的移動方向。依據實驗結果，我們提出的方法能夠有效的修正方向，讓球體往受測者評估的正確路徑移動，並且明顯地比之前的找尋迷宮路徑演算法花費更少的步數即可到達終點。我們證明在腦波分類無法準確傳達受測者意志的狀況下，透過協同控制策略依然能讓球體移動到受測者所想的位置並且為腦波控制移動方向的應用提供新的觀點。

In this paper we present a method of shared control strategy which combine electroencephalography (EEG) signals and heuristic algorithm, to guide a virtual dot on the two dimensional maze to reach the goal. One of the most main problems of EEG-based brain computer interfaces (BCIs) is the low classify accuracy. It is hard to build a generalize classifier to identify all the people’s EEG signals, in order to using people’s intention as the control factor with the low information rate, recent works have explored shared-control strategies which the system does not only execute the decoded commands from signals’ owner, but also involved in executing the task have been set up beforehand. That is, the system’s execution result can more close to people’s intension. Our shared-control system use error-related potentials (ErrP) as feedback which only be detected when the subjects feel wrong or confused. ErrP can be evoked steady in 0.3~0.6 milliseconds after the stimulation happened, with the subjects’ assessment of the target moving in the maze, transfer them as feedback into our heuristic function, we can guide the target to reach the goal without knowing the goal’s location efficiently. Shared-control strategies in BCI systems such as we presented here may prove to be the foundation for complex BCIs capable of doing more than we ever imagined.

摘要 I
Abstract II
1.INTRODUCTION 2
2. BACKGROUND and RELATED WORK 5
2.1 Background of Brain Computer Interface 5
2.2 Background of Electroencephalography 6
2.2.1 EEG 6
2.2.2 ERP 7
2.2.3 ERN 7
2.3 Shared-Control Strategies 9
3. METHODS 11
3.1 Data Recording 12
3.2 Feature extraction 14
3.3 Experimental protocols 19
3.3.1 Training phase: 20
3.3.2 Control phase: 23
4.EXPERIMENT RESULT 35
4.1 Electrophysiology analysis 35
4.2 Control phase analysis 37
4.2.1 Number of Steps 38
4.2.2 Classifier Accuracy 42
5. CONCLUSION 44
6. REFERENCE 46

[1] Vidal, J. J. (1973). Toward direct brain-computer communication. Annual review of Biophysics and Bioengineering, 2(1), 157-180.
[2] Vidal, J. J. (1977). Real-time detection of brain events in EEG. Proceedings of the IEEE, 65(5), 633-641.
[3] Trieu, H. T., Nguyen, H. T., & Willey, K. (2008, August). Shared control strategies for obstacle avoidance tasks in an intelligent wheelchair. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (pp. 4254-4257). IEEE.
[4] Campbell, A., Choudhury, T., Hu, S., Lu, H., Mukerjee, M. K., Rabbi, M., & Raizada, R. D. (2010, August). Neuro Phone: brain-mobile phone interface using a wireless EEG headset. In Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds (pp. 3-8). ACM.
[5] Astolfi , L., Cincotti, F., Mattia, D., Marciani, M. G., Baccala, L. A., de Vico Fallani, F., ... & Babiloni, F. (2007). Comparison of different cortical connectivity estimators for high‐resolution EEG recordings. Human brain mapping, 28(2), 143-157.
[6] Kamiński, M., Ding, M., Truccolo, W. A., & Bressler, S. L. (2001). Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biological cybernetics, 85(2), 145-157.
[7] Granger, C. W. (1988). Some recent development in a concept of causality.Journal of econometrics, 39(1), 199-211.
[8] Hesse, W., Möller, E., Arnold, M., & Schack, B. (2003). The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. Journal of neuroscience methods, 124(1), 27-44.
[9] Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In Selected Papers of Hirotugu Akaike (pp. 199-213). Springer New York.
[10] Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association, 77(378), 304-313.
[11] Kaminski, M. J., & Blinowska, K. J. (1991). A new method of the description of the information flow in the brain structures. Biological cybernetics, 65(3), 203-210.
[12] Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST),2(3), 27.
[13] Hjelm, S. I., & Browall, C. (2000, October). Brainball-using brain activity for cool competition. In Proceedings of NordiCHI (Vol. 7, p. 9).
[14] Menon, V., Ford, J. M., Lim, K. O., Glover, G. H., & Pfefferbaum, A. (1997). Combined event-related fMRI and EEG evidence for temporal-parietal cortex activation during target detection. Neuroreport, 8(14), 3029-3037.
[15] Yeung, N., Botvinick, M. M., & Cohen, J. D. (2004). The neural basis of error detection: conflict monitoring and the error-related negativity. Psychological review, 111(4), 931.
[16] Lee, J. C., & Tan, D. S. (2006, October). Using a low-cost electroencephalograph for task classification in HCI research. In Proceedings of the 19th annual ACM symposium on User interface software and technology(pp. 81-90). ACM.
[17] Lee, C. Y. (1961). An algorithm for path connections and its applications.Electronic Computers, IRE Transactions on, (3), 346-365.
[18] Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. Systems Science and Cybernetics, IEEE Transactions on, 4(2), 100-107.
[19] Vi, C., & Subramanian, S. (2012, May). Detecting error-related negativity for interaction design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 493-502). ACM.
[20] Royer, A. S., Doud, A. J., Rose, M. L., & He, B. (2010). EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 18(6), 581-589.
[21] Ramirez-Cortes, J. M., Alarcon-Aquino, V., Rosas-Cholula, G., Gomez-Gil, P., & Escamilla-Ambrosio, J. (2011). Anfis-based p300 rhythm detection using wavelet feature extraction on blind source separated eeg signals. In Intelligent Automation and Systems Engineering (pp. 353-365). Springer New York.