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
<br>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。
<br>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
<br>穿戴式設備，實際的搜集與分析資料。
<br>
<br>In this subject, we will explore these technologies and approaches for acquiring and then translating brain 
<br>activity into useful information. We will also discuss the components of a brain-computer interface (BCI) 
<br>system, invasive and non-invasive neural interfaces, the clinical and practical applications for a variety of 
<br>users, and the ethical considerations of interfacing with the brain. The topics includes basics of brain wave 
<br>(EEG), BCI, signal processing, data mining, and also contains hands-on tutorial and laboratory session on 
<br>using wearable device to collect and analyse EEG data.
<br>
<br>二、指定用書(Text Books)
<br>None.
<br>
<br>三、參考書籍(References)
<br>1.	J. Wolpaw and E. W. Wolpaw, Brain-Computer Interfaces: Principles and Practice, Oxford University 
<br>Press, 2011.
<br>2.	L. F. Nicolas-Alonso and J. Gomez-Gil, Brain Computer Interfaces, a Review, Sensors, 12, 2012, 
<br>1211-1279.
<br>3.	C. Kothe, BCILAB, https://sccn.ucsd.edu/wiki/BCILAB.
<br>
<br>四、教學方式(Teaching Method)
<br>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
<br>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
<br>料。
<br>
<br>Subject presentation includes combined lecture, tutorial and laboratory sessions and research and 
<br>development work for the assignments. The tutorial sessions focus on hands-on experience in brain data 
<br>analytics tools, and understanding and interpretation of the results. The laboratory sessions focus on 
<br>hands-on experience in carrying out BCI experiments and collecting high quality data.
<br>
<br>五、教學進度(Syllabus)
<br>1	Introduction to BCI Design
<br>2	EEG Basics
<br>3	Data Collection
<br>4	Signal Processing in EEG
<br>5	EEGLAB Basics
<br>6	EEG Channel Spectra and Maps
<br>7	Pre-processing
<br>8	Presentation
<br>9	Artefact Removal
<br>10	Blind Source Separation
<br>11	Time/Frequency Decomposition
<br>12	ERP-ba<x>sed BCIs & SSVEP-ba<x>sed BCIs
<br>13	BCI applications
<br>14	Feature Extraction and Classification
<br>15	Brain Network Analysis
<br>16	Final
<br>
<br>
<br>六、成績考核(Evaluation)
<br>}39;Assessment task 1: Homework/Assignments (20%)
<br>}39;Assessment task 2: The BCI consultant (35%)
<br>Note: This assignment is a group project (form a team of 3) where students are given a business 
<br>(industrial) or scientific problem and need to write a project proposal for approaching that problem by the 
<br>means of BCIs. 
<br>Students must also present a 15 minute pitch for the project in week 8.
<br>}39;Assessment task 3: EEG Data exploration, preparation and mining in action (45%)
<br>Note: This assignment includes practical work on EEG data visualisation, exploration and preparation 
<br>(preprocessing and transformation) for EEG data analytics. Students must also present a 15 minute pitch 
<br>for the project in week 16.
<br>
<br>
<br>Ethics Statement on Generative Artificial Intelligence
<br>
<br>Grounded in the principles of transparency and responsibility, this course encourages students to leverage 
<br>AI for collaboration and mutual learning to enhance the quality of course outputs. In accordance with the 
<br>published Guidelines for Collaboration, Co-learning, and Cultivation of Artificial Intelligence Competencies 
<br>in University Education, this course adopts the following policy：Conditionally open
<br>
<br>Students may briefly explain how generative AI was used for topic ideation, sentence refinement, or 
<br>structural reference in the footnotes of the ti<x>tle page or after the bibliography in their assignments or 
<br>reports. However, in the "personal reflection report" and "group interview assignment" of this course, 
<br>students are not allowed to use generative AI tools for writing assignments. If usage is discovered without 
<br>proper disclosure, instructors, the institution, or relevant units have the right to reevaluate the assignment 
<br>or report or withhold scores. If the course materials or learning resources have been derived from 
<br>generative AI, the instructor will also indicate this in the slides or orally. Students enrolled in this course 
<br>agree to the above ethics statement if registering for the class.
<br>