近幾年來腦波的應用不論是在情緒識別，又或是醫療臨床上的應用及各領域都越來越普及，透過圖片特徵進行情緒識別的研究已有不少人證明其可行性。也有研究證明經由對應情緒之腦波訊號的統計值作為特徵進行情緒辨識準確率皆高達90%以上。鑒於過往學者之研究，本研究欲探究由情緒圖片特徵產生某特定受測者之腦波訊號的可能性。其作法為經由深度學習技術來模擬受測者觀看一系列情緒圖片後而產生之腦波訊號腦波。我們使用深度學習技術，在預測模型訓練階段把情緒圖片之特徵作為輸入層之輸入值，與該圖片對應之腦波訊號作為輸出層的輸出值，以此得到預測模型，在測試階段以受測者觀看同類型情緒圖片所產生的腦波訊號去評估預測模型的有效性。若可以模擬一個人的腦波，除了可以作為測試生物識別系統的技術。在人工智慧的領域上相信也能有更廣泛的應用以及不可限量的發展。

In recent years, using brainwaves in emotion recognition and medical and clinical applications has become more popular. Literature has proved the feasibility of emotion recognition through image features. In the literature, the accuracy of emotion recognition is as high as 90% by using the statistical value of the corresponding emotional brainwave signals. Given previous research, this study intends to explore the possibility of generating the brainwave signals of a specific subject from the features of emotional pictures. The proposed method uses deep learning technology to simulate the brainwave signals generated by the subjects after watching a series of emotional pictures. In the training stage of the prediction model, the features of the emotional picture are used as the input value of the input layer, and the brainwave signal corresponding to the picture is used as the output value of the output layer to obtain the prediction model. The brainwave signals from the same subjects were used to evaluate the validity of the prediction model. If we can simulate a person's brainwaves, it can be used for testing biometric systems. We also believe that simulating brainwave signals have broader applications and unlimited development in artificial intelligence area.

第一章 緒論 1
1.1 研究動機與背景 1
1.2 研究目的 3
第二章 文獻探討 5
2.1 腦波簡介 5
2.1.1 神經細胞電位 5
2.1.2 腦電圖的簡介 6
2.1.3 腦波干擾問題 8
2.2 現代腦電圖設備 9
2.2.1 腦機介面 9
2.2.2 Neurosky MindWave Mobile 2耳機 9
2.2.3 電極配置 12
2.3 情緒辨識與分析 13
2.3.1 情緒辨識的概論 13
2.3.2 情感識別的方式 14
2.3.3 基於圖片特徵的情緒識別 15
2.4 深度學習 17
2.4.1遞迴神經網路(Recurrent Neural Network, RNN) 17
2.4.2長短期記憶(Long Short-Term Memory, LSTM) 18
第三章 研究方法 21
3.1 研究架構 21
3.2 研究工具 26
3.2.1 腦波訊號量測儀器 26
3.2.2 軟硬體開發工具 26
3.3 實驗設計 26
3.3.1 純色圖靜置實驗比較結果 27
3.4 資料前處理 29
3.4.1 訊號採集服務器 29
3.4.2 OpenViBE Designer 30
3.5 特徵擷取 31
3.5.2 基於EMD計算之色彩 32
3.5.3 Tamura的紋理特徵 32
3.5.4 基於GLCM計算之紋理特徵 34
3.5.5 人臉特徵 35
第四章 實驗結果 37
4.1 以LSTM作為機器學習模型之實驗結果 37
4.2 三種模型比較之實驗結果 40
4.3 研究限制 44
第五章 結論與未來展望 45
5.1 結論 45
5.2 未來展望 45
參考文獻 47

[1]M. Murugappan, N. Ramachandran, and Y. Sazali, "Classification of human emotion from EEG using discrete wavelet transform," Journal of biomedical science and engineering, vol. 3, no. 04, p. 390, 2010.
[2]W. Weng and K. Khorasani, "An adaptive structure neural networks with application to EEG automatic seizure detection," Neural Networks, vol. 9, no. 7, pp. 1223-1240, 1996.
[3]G. Pfurtscheller, G. R. Müller, J. Pfurtscheller, H. J. Gerner, and R. Rupp, "‘Thought’–control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia," Neuroscience letters, vol. 351, no. 1, pp. 33-36, 2003.
[4]S. Narayana, R. V. Prasad, and K. Warmerdam, "Mind your thoughts: BCI using single EEG electrode," IET Cyber-Physical Systems: Theory & Applications, vol. 4, no. 2, pp. 164-172, 2019.
[5]S. Jerritta, M. Murugappan, R. Nagarajan, and K. Wan, "Physiological signals based human emotion recognition: a review," in 2011 IEEE 7th International Colloquium on Signal Processing and its Applications, 2011, pp. 410-415: IEEE.
[6]G. L. Ahern and G. E. Schwartz, "Differential lateralization for positive and negative emotion in the human brain: EEG spectral analysis," Neuropsychologia, vol. 23, no. 6, pp. 745-755, 1985.
[7]D. Mathersul, L. M. Williams, P. J. Hopkinson, and A. H. Kemp, "Investigating models of affect: relationships among EEG alpha asymmetry, depression, and anxiety," Emotion, vol. 8, no. 4, p. 560, 2008.
[8]V. Bajaj and R. B. Pachori, "Human emotion classification from EEG signals using multiwavelet transform," in 2014 International Conference on Medical Biometrics, 2014, pp. 125-130: IEEE.
[9]S. Morshad, M. R. Mazumder, and F. Ahmed, "Analysis of Brain Wave Data Using Neurosky Mindwave Mobile II," in Proceedings of the International Conference on Computing Advancements, 2020, pp. 1-4.
[10]J. Machajdik and A. Hanbury, "Affective image classification using features inspired by psychology and art theory," in Proceedings of the 18th ACM international conference on Multimedia, 2010, pp. 83-92.
[11]P. J. Lang, "International affective picture system (IAPS): Affective ratings of pictures and instruction manual," Technical report, 2005.
[12]C. Mühl, B. Allison, A. Nijholt, and G. Chanel, "A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges," Brain-Computer Interfaces, vol. 1, no. 2, pp. 66-84, 2014.
[13]R. Datta, D. Joshi, J. Li, and J. Z. Wang, "Studying aesthetics in photographic images using a computational approach," in European conference on computer vision, 2006, pp. 288-301: Springer.
[14]A. Hanjalic and L.Q. Xu, "Affective video content representation and modeling," IEEE transactions on multimedia, vol. 7, no. 1, pp. 143-154, 2005.
[15]H. Tamura, S. Mori, and T. Yamawaki, "Textural features corresponding to visual perception," IEEE Transactions on Systems, man, and cybernetics, vol. 8, no. 6, pp. 460-473, 1978.
[16]R. M. Haralock and L. G. Shapiro, Computer and robot vision. Addison-Wesley Longman Publishing Co., Inc., 1991.
[17]C. Liensberger, J. Stottinger, and M. Kampel, "Color-based and context-aware skin detection for online video annotation," in 2009 IEEE International Workshop on Multimedia Signal Processing, 2009, pp. 1-6: IEEE.
[18]H. Lecar and R. Nossal, "Theory of threshold fluctuations in nerves: II. Analysis of various sources of membrane noise," Biophysical journal, vol. 11, no. 12, pp. 1068-1084, 1971.
[19]H. L. M. W. A. Atwood, Essentials of neurophysiology. Toronto: B.C. Decker, 1989.
[20]B. Alberts et al., Molecular biology of the cell. WW Norton & Company, 2017.
[21]L. Abdul Kadir, M. Stacey, and R. Barrett-Jolley, "Emerging roles of the membrane potential: action beyond the action potential," Frontiers in physiology, vol. 9, p. 1661, 2018.
[22]R. Cooper, J. W. Osselton, and J. C. Shaw, EEG technology. Butterworth-Heinemann, 2014.
[23]J. D. Bronzino, Biomedical Engineering Handbook 2. Springer Science & Business Media, 2000.
[24]M. Teplan, "Fundamentals of EEG measurement," Measurement science review, vol. 2, no. 2, pp. 1-11, 2002.
[25]J. C. Henry, "Electroencephalography: basic principles, clinical applications, and related fields," Neurology, vol. 67, no. 11, pp. 2092-2092-a, 2006.
[26]G. S. NAHM, J. PETERSEN, H. GEHRING, E. KONECNY, H.D. KOCHS, E. KOCHS, W "Concept for an intelligent anaesthesia EEG monitor," Medical informatics and the internet in medicine, vol. 24, no. 1, pp. 1-9, 1999.
[27]W. J. Nowack, "Neocortical dynamics and human EEG rhythms," Neurology, vol. 45, no. 9, pp. 1793-1793-a, 1995.
[28]F. S. Tyner and J. R. Knott, Fundamentals of EEG Technology: Basic concepts and methods. Lippincott Williams & Wilkins, 1983.
[29]S. Palva and J. M. Palva, "New vistas for a-frequency band oscillations," Trends in Neurosciences, vol. 30.
[30]L. Poupard, R. Sartène, and J.C. Wallet, "Scaling behavior in β-wave amplitude modulation and its relationship to alertness," Biological Cybernetics, vol. 85, no. 1, pp. 19-26, 2001.
[31]T. Liu, J. Shi, D. Zhao, and J. Yang, "The event‐related low‐frequency activity of highly and average intelligent children," High Ability Studies, vol. 19, no. 2, pp. 131-139, 2008.
[32]Y. Zhang, R. R. Llinas, and J. Lisman, "Inhibition of NMDARs in the nucleus reticularis of the thalamus produces delta frequency bursting," Frontiers in neural circuits, vol. 3, p. 20, 2009.
[33]關尚勇 and 林吉和, "破解腦電波," 台北市: 藝軒, 2002.
[34]P. Sauseng, J. Hoppe, W. Klimesch, C. Gerloff, and F. C. Hummel, "Dissociation of sustained attention from central executive functions: local activity and interregional connectivity in the theta range," European Journal of Neuroscience, vol. 25, no. 2, pp. 587-593, 2007.
[35]B. He, Neural engineering. Springer, 2005.
[36]B. Graimann, B. Allison, and G. Pfurtscheller, "Brain–computer interfaces: A gentle introduction," in Brain-computer interfaces: Springer, 2009, pp. 1-27.
[37]洪偉哲, "以小波轉換鑑別人類情緒腦電波," 臺灣師範大學機電科技研究所學位論文, pp. 1-110, 2011.
[38]Neurosky. (2015). Minwave. Available: https://store.neurosky.com/pages/mindwave
[39]Neurosky. (2016). Mindwave product market. Available: http://www.neurosky.com.tw/products-markets/eeg-biosensors/hardware
[40]林建豪, "腦波應用程式開發教學教材之編撰," 朝陽科技大學資訊工程系學位論文, pp. 1-50, 2015.
[41]A. Khosla, P. Khandnor, and T. Chand, "A comparative analysis of signal processing and classification methods for different applications based on EEG signals," Biocybernetics and Biomedical Engineering, vol. 40, no. 2, pp. 649-690, 2020.
[42]H. H. Jasper, "The ten-twenty electrode system of the International Federation," Electroencephalogr. Clin. Neurophysiol., vol. 10, pp. 370-375, 1958.
[43]M. K. Abadi, R. Subramanian, S. M. Kia, P. Avesani, I. Patras, and N. Sebe, "DECAF: MEG-based multimodal database for decoding affective physiological responses," IEEE Transactions on Affective Computing, vol. 6, no. 3, pp. 209-222, 2015.
[44]J. Wagner, "Augsburg biosignal toolbox (aubt)," University of Augsburg, 2005.
[45]P. Ekman, "Are there basic emotions?," 1992.
[46]D. Novak, M. Mihelj, and M. Munih, "A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing," Interacting with computers, vol. 24, no. 3, pp. 154-172, 2012.
[47]R. Plutchik, "The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice," American scientist, vol. 89, no. 4, pp. 344-350, 2001.
[48]J. A. Russell and A. Mehrabian, "Evidence for a three-factor theory of emotions," Journal of research in Personality, vol. 11, no. 3, pp. 273-294, 1977.
[49]J. A. Russell, "A circumplex model of affect," Journal of personality and social psychology, vol. 39, no. 6, p. 1161, 1980.
[50]B. R. Nhan and T. Chau, "Classifying affective states using thermal infrared imaging of the human face," IEEE Transactions on Biomedical Engineering, vol. 57, no. 4, pp. 979-987, 2009.
[51]S. Koelstra et al., "Deap: A database for emotion analysis; using physiological signals," IEEE transactions on affective computing, vol. 3, no. 1, pp. 18-31, 2011.
[52]H. Gunes and M. Piccardi, "A bimodal face and body gesture database for automatic analysis of human nonverbal affective behavior," in 18th International conference on pattern recognition (ICPR'06), 2006, vol. 1, pp. 1148-1153: IEEE.
[53]R. Jenke, A. Peer, and M. Buss, "Feature extraction and selection for emotion recognition from EEG," IEEE Transactions on Affective computing, vol. 5, no. 3, pp. 327-339, 2014.
[54]W. Wang and Q. He, "A survey on emotional semantic image retrieval," in 2008 15th IEEE International Conference on Image Processing, 2008, pp. 117-120: IEEE.
[55]A. Hanjalic, "Extracting moods from pictures and sounds: Towards truly personalized TV," IEEE Signal Processing Magazine, vol. 23, no. 2, pp. 90-100, 2006.
[56]M. S. Lew, N. Sebe, C. Djeraba, and R. Jain, "Content-based multimedia information retrieval: State of the art and challenges," ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 2, no. 1, pp. 1-19, 2006.
[57]P. Valdez and A. Mehrabian, "Effects of color on emotions," Journal of experimental psychology: General, vol. 123, no. 4, p. 394, 1994.
[58]N. Bianchi Berthouze, "K-DIME: an affective image filtering system," IEEE MultiMedia, vol. 10, no. 3, pp. 103-106, 2003.
[59]J. Itten, The elements of color. John Wiley & Sons, 1970.
[60]V. Yanulevskaya, J. C. van Gemert, K. Roth, A.K. Herbold, N. Sebe, and J.M. Geusebroek, "Emotional valence categorization using holistic image features," in 2008 15th IEEE international conference on Image Processing, 2008, pp. 101-104: IEEE.
[61]T. Hayashi and M. Hagiwara, "Image query by impression words-the IQI system," IEEE Transactions on Consumer Electronics, vol. 44, no. 2, pp. 347-352, 1998.
[62]P. J. Lang, M. M. Bradley, and B. N. Cuthbert, "International affective picture system (IAPS): Technical manual and affective ratings," NIMH Center for the Study of Emotion and Attention, vol. 1, no. 39-58, p. 3, 1997.
[63]H. Sak, A. Senior, and F. Beaufays, "Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition," arXiv preprint arXiv, 2014.
[64]M. Liu, R. Wang, S. Li, S. Shan, Z. Huang, and X. Chen, "Combining multiple kernel methods on riemannian manifold for emotion recognition in the wild," in Proceedings of the 16th International Conference on multimodal interaction, 2014, pp. 494-501.
[65]S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[66]R. J. Williams and D. Zipser, "Gradient-based learning algorithms for recurrent," Backpropagation: Theory, architectures, and applications, vol. 433, p. 17, 1995.
[67]G. Zhao and M. Pietikainen, "Dynamic texture recognition using local binary patterns with an application to facial expressions," IEEE transactions on pattern analysis and machine intelligence, vol. 29, no. 6, pp. 915-928, 2007.
[68]J. Bayer, D. Wierstra, J. Togelius, and J. Schmidhuber, "Evolving memory cell structures for sequence learning," in International Conference on Artificial Neural Networks, 2009, pp. 755-764: Springer.
[69]M. Murugappan, M. Rizon, R. Nagarajan, and S. Yaacob, "Inferring of human emotional states using multichannel EEG," European Journal of Scientific Research, vol. 48, no. 2, pp. 281-299, 2010.
[70]E. T. Mampusti, J. S. Ng, J. J. I. Quinto, G. L. Teng, M. T. C. Suarez, and R. S. Trogo, "Measuring academic affective states of students via brainwave signals," in 2011 Third International Conference on Knowledge and Systems Engineering, 2011, pp. 226-231: IEEE.
[71]H. Gunes and M. Pantic, "Automatic, dimensional and continuous emotion recognition," International Journal of Synthetic Emotions, vol. 1, no. 1, pp. 68-99, 2010.
[72]A. Hanbury, "Constructing cylindrical coordinate colour spaces," Pattern Recognition Letters, vol. 29, no. 4, pp. 494-500, 2008.
[73]C. E. Osgood, G. J. Suci, and P. H. Tannenbaum, The measurement of meaning (no. 47). University of Illinois press, 1957.
[74]K. Hayes Jr, A. Shah, and A. Rosenfeld, "Visual Texture Analysis IV," Maryland Univ College Park Computer Science Center, 1973.
[75]M. Hall Beyer, "GLCM texture: a tutorial," National Council on Geographic Information and Analysis Remote Sensing Core Curriculum, vol. 3, p. 75, 2000.
[76]R. Zabih and J. Woodfill, "A non-parametric approach to visual correspondence," in IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996: Citeseer.
[77]B. Froba and A. Ernst, "Face detection with the modified census transform," in Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., 2004, pp. 91-96: IEEE.
[78]B. Jun and D. Kim, "Robust real-time face detection using face certainty map," in International Conference on Biometrics, 2007, pp. 29-38: Springer.
[79]Z. Rasheed, Y. Sheikh, M. Shah, "On the use of computable features for film classification," Transactions on Circuits Systems for Video Technology, vol. 15, no. 1, pp. 52-64, 2005.