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課程名稱 |
大腦事件誘發電位資料收集與分析導論
Introduction to Event-Related Potential (ERP) data collection and analysis |
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開課學期 |
110-1 |
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授課對象 |
學程 神經生物與認知科學學程 |
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授課教師 |
李佳霖 |
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課號 |
LING7428 |
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課程識別碼 |
142 M1160 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期一6,7,8(13:20~16:20) |
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上課地點 |
樂學館304 |
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備註 |
本課程中文授課,使用英文教科書。歡迎大學部同學選修。
總人數上限：12人
外系人數限制：4人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
Please see below. |
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課程目標 |
Course Description and Goals: This course aims to provide fundamentals about ERP research and hands-on experience of data-collection and data analysis for beginner students. This course will be a mix of lectures, substantial laboratory sessions, individual consultations, and discussions. Students will have an opportunity to collect ERP data from peers in the class using a well-established ERP paradigm, and analyze the data based on the methodology covered in this course. |
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課程要求 |
Students will be required to collect ERP data from peers in the class using a well-established ERP paradigm, analyze the data, and present the analysis result at the end of the semester. |
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預期每週課後學習時數 |
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Office Hours |
另約時間 備註： This course will be conducted online using google meet (meet.google.com/fwi-nvkd-ebx) for the first three weeks. For those who wish to manually register, please attend the first class and we will disseminate the registration info then.
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指定閱讀 |
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參考書目 |
We will be using the first book as our textbook, but you are strongly encouraged to read the other two books as well, as [2] provides different illustration of similar concepts and [3] provides state-of-the-art summary on important ERP components.
1. Luck, Steven J. An introduction to the event-related potential technique. (2nd edition). Cambridge, MA:MIT press, 2014.
2. Handy, Todd C., ed. Event-related potentials: A methods handbook. MIT press, 2005.
3. Luck, S. J. & Kappenman, E. S. (Eds.) (2012). The Oxford Handbook of Event-Related Potential Components, New York: Oxford University Press.
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
Take home mid-term exam |
25% |
The mid-term exam will cover the materials in the textbook as well as the lectures. |
2. |
Review questions |
15% |
A short google form comprehension check about the lecture will be given after each video. |
3. |
Homework |
25% |
Following each lab session, students will be asked to practice the analysis. If a student submits an assignment after the due date without having made arrangements with the instructor, 1 point will be deducted for each day, or part thereof, that the assignment is late.
You are encouraged to study together and to discuss information and concepts covered in class with other students. You can give "consulting" help to or receive "consulting" help from such students. However, this should never involve one student having possession of a copy of all or part of work done by someone else.
* Deliberate plagiarism is an extremely serious offense that may result in failing the course. Plagiarism includes not only copying from a published source or using internet materials without appropriate acknowledgment, but also presenting another student’s work as your own.
Should copying occur, both the student who copied work from another student and the student who gave material to be copied will both automatically receive a zero on the assignment. Depending on the seriousness of the offense, we might let the university handle the case as well. |
4. |
Take home final exam |
25% |
Students will be asked to analyze a dataset using set parameters for this exam. Exams are not cumulative. |
5. |
Class participation |
10% |
Students can greatly contribute to the course by actively asking questions in class. Participating in class discussions is valuable because it makes you an active learner and greatly increases the likelihood that you will understand and retain the material. |
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週次 |
日期 |
單元主題 |
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第1週 |
9/27 |
Overview
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第2週 |
10/4 |
Lecture: A broad overview of the Event-Related Potential technique
Video: lab tour.
Lecture: A closer look at ERPs and ERP components
[Reading: Luck Ch1&2 ] |
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第3週 |
10/11 |
No class |
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第4週 |
10/18 |
Lecture: Overview of common ERP components
[Reading: Luck Ch3 ] |
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第5週 |
10/25 |
Lecture: The design of ERP experiments
[Reading: Luck Ch4 ] |
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第6週 |
11/1 |
Lecture: Setting up an ERP lab & Basic Principles of ERP recording
[Reading: Luck Ch5 & 16] |
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第7週 |
11/8 |
[Mid term exam]
Lab (computers required): Getting started with ERPLAB/trouble shooting |
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第8週 |
11/15 |
校慶 NO CLASS |
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第9週 |
11/22 |
Lecture: Mid-term Q&A; Overview of data analysis
Lecture & Lab (computers required): [Load Dataset, Create Event List (Binlister), assign bins]
[HW1: bin description; re-reference due on 11/27] |
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第10週 |
11/29 |
Lecture & Lab (computers required): assign bins, Epoching, Baseline correction, Channel operations, re-referencing
[Reading: Luck Ch6]
[HW2&3: re-reference due on 12/4] |
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第11週 |
12/6 |
Lecture & Lab (computers required): Artifact Rejection, Artifact Correction ＆ ICA tutorial and practice;
[Reading: Luck Ch8 (a bit of Ch11)]
[HW4 artifact detection due on 12/11] |
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第12週 |
12/13 |
Lecture & Lab (computers required):
ICA practice
Averaging
Practice
Filtering
[Reading: Luck Ch7 (a bit of Ch12)] |
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第13週 |
12/20 |
Lecture: Quantifying ERPs
[Reading: Luck Ch9]
Lab (computers required): Data visualization (Plot waveforms, difference waves, scalp maps)
[HW5 average and filter due on 12/25] |
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第14週 |
12/27 |
Lab (computers required): practice analyzing one dataset from scratch using GUI
(ERP analysis) |
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第15週 |
1/3 |
Lecture: Statistical analysis
[Reading: Luck Ch10]
Lab (computers required): analyzing data using script, data analysis practice loop, insert codes |
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第16週 |
1/10 |
Take home final's exam/ |
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