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.