<<開課給研究所同學，教育學院學士班同學待大學部開課再修>>



這門課我們將探索如何搜集大腦活動以及如何轉譯這些資料成為有用的資訊。我們也會討論到腦機介面中幾個重要的



元件與步驟、侵入與非侵入方法、針對不同使用者的臨床與實際應用，以及與大腦互動時所該考慮的倫理。主題含括



腦波(EEG)的原理、腦機介面、訊號處理、資料探勘，另外我們還會有實驗課，可以動手操作最新的無線穿戴式設



備，實際的搜集與分析資料。In this subject, we will explore these technologies and approaches for acquiring



and then translating brain activity into useful information. We will also discuss the components of a brain-



computer interface (BCI) system, invasive and non-invasive neural interfaces, the clinical and practical



applications for a variety of users, and the ethical considerations of interfacing with the brain. The topics



includes basics of brain wave (EEG), BCI, signal processing, data mining, and also contains hands-on tutorial



and laboratory session on using wearable device to collect and analyse EEG data.











二、指定用書(Text Books)







None.















三、參考書籍(References)







1. J. Wolpaw and E. W. Wolpaw, Brain-Computer Interfaces: Principles and Practice, Oxford University



Press, 2011.







2. L. F. Nicolas-Alonso and J. Gomez-Gil, Brain Computer Interfaces, a Review, Sensors, 12, 2012, 1211-



1279.







3. C. Kothe, BCILAB, https://sccn.ucsd.edu/wiki/BCILAB.















四、教學方式(Teaching Method)







這門課結合講授、個別指導與實驗以完成作業中的研發任務為目標。其中在個別指導時段，我們將著墨在資料分析與



闡述結果的實際經驗；在實驗時段，學生將會有機會動手執行腦機介面實驗，學習收集高品質大腦腦波資料。







Subject presentation includes combined lecture, tutorial and laboratory sessions and research and



development work for the assignments. The tutorial sessions focus on hands-on experience in brain data



analytics tools, and understanding and interpretation of the results. The laboratory sessions focus on hands-



on experience in carrying out BCI experiments and collecting high quality data.















五、教學進度(Syllabus)







1 Introduction to BCI Design







2 EEG Basics







3 Data Collection







4 Signal Processing in EEG







5 EEGLAB Basics







6 EEG Channel Spectra and Maps







7 Pre-processing







8 Presentation







9 Artefact Removal







10 Blind Source Separation







11 Time/Frequency Decomposition







12 ERP-based BCIs & SSVEP-based BCIs







13 BCI applications







14 Feature Extraction and Classification







15 Brain Network Analysis







16 Other Brain Imaging Modalities & Ethics















六、成績考核(Evaluation)







Assessment task 1: Your class activity (20%)







Note: Demonstrate your understanding of BCIs or EEG basics. Contribute to in-class activity and discussion.















Assessment task 2: The BCI consultant (35%)







Note: This assignment is a group project (form a team of 3) where students are given a business (industrial)



or scientific problem and need to write a project proposal for approaching that problem by the means of BCIs.



Students must also present a 15 minute pitch for the project in week 9.















Assessment task 3: EEG Data exploration, preparation and mining in action (45%)







Note: This assignment includes practical work on EEG data visualisation, exploration and preparation



(preprocessing and transformation) for EEG data analytics. Students must also present a 15 minute pitch for



the project in week 16.