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過去(2018年度)の授業の情報です
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最終更新日:2022年4月21日

Brain and Behavioral Measurement for Psychological Experiment

The experimental lecture series will be given by Dr. Rutkowski who is a research scientist in an AI startup Cogent Labs Inc. in Tokyo. He also serves as a research fellow at The University of Tokyo. The lecture course objective will introduce behavioral and brain-science-related experimental designs and result analysis techniques. Each of the class will have several hands-on practical examples for students to practically learn or design and execute own small experimental paradigms. Results analysis techniques will be also overviewed using state-of-the-art analytical methods. Final classes will overview current hot and more futuristic experimental problems including AI as well as intelligence augmentation methods.
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時間割/共通科目コード
コース名
教員
学期
時限
09185305
FED-DS3403S1
Brain and Behavioral Measurement for Psychological Experiment
Rutkowski Maciej Tomasz
S1 S2
月曜5限
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教室
図書館研究所学部等 教育学部棟・159講義室
講義使用言語
英語
単位
2
実務経験のある教員による授業科目
NO
他学部履修
開講所属
教育学部
授業計画
1. Introduction, lecture series target, content details and explanation of simple programing tools to be used during lectures. 2. Experimental tools design and measurement of behavioral responses (capturing audio, video, body movement and peripheral physiological signals such as EMG/EOG/etc.) - hands-on experimental examples included. 3. Introduction to experimental computational neuroscience and brainwave measurement methods (EEG/PET/fMRI/etc.) - hands-on experimental examples included. 4. Experiment theory. 5. Experimental stimulus setup (visual, auditory, tactile, etc.) and simple visual programming examples (MAX environment, etc.) - hands-on experimental examples included. 6. Experimental recording setup (behavioral and computational neuroscience methods) for successful results - hands-on experimental examples included. 7. EEG hands on session in small groups (simple brainwave recording design and execution in order to understand proper signal capturing problems) - a fully hands-on lecture. 8. Introduction to behavioral data analysis tools and methods (audio, video, EOG, EMG, etc.). 9. Introduction to brainwave data analysis tools and methods (EEG). 10. Own experiment design in small groups (stimulus and data recording procedures) - a fully hands-on lecture. 11. Future methods of experimental research using AI and deep-learning tools. 12. Closed loop neurotechnology and “digital-phrama” methods for experiments. 13. Brain-computer interfacing in psychological experimental setup. 14. Lecture summary and student own/requested experiments - a hands-on session. 15. Final quiz and Q&A session.
授業の方法
1. Lecture. 2. Simple programming and setup hands-on experimentation in the class with provided equipment as well as executed by the students. 3. Simple brainwave (EEG) recording experiments and results analysis by the students.
成績評価方法
1. Attendance = 30% 2. Class quizzes = 20% 3. Experimental exercises = 20% 4. Final quiz = 30%
教科書
1. Cunningham DW, Wallraven C. Experimental design: From user studies to psychophysics. CRC Press; 2011 Nov 17. 2. Wolpaw J, Wolpaw EW, editors. Brain-computer interfaces: principles and practice. OUP USA; 2012 Jan 24.
参考書
1. Rutkowski T. Robotic and Virtual Reality BCIs Using Spatial Tactile and Auditory Oddball Paradigms. Frontiers in Neurorobotics. 2016;10:20. 2. Rutkowski TM. Student Teaching and Research Laboratory Focusing on Brain–computer Interface Paradigms – A Creative Environment for Computer Science Students –. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. IEEE Press; 2015. p. 3667-3670. 3. Huggins JE, Guger C, Allison B, Anderson CW, Batista A, Brouwer AM, et al. Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future. Brain-Computer Interfaces. 2014;1(1):27-49.
履修上の注意
Students with interest in learning how to design, conduct and analyze psychological behavioral or computational neuroscience experiments.