Lead-in to Brain-Computer Interface. How to measure BioData

The main idea of the course is that while we rely on AI, it is crucial for EEG analysis to have clean data. This is because EEG datasets are usually limited, and if the data is noisy, it becomes extremely difficult for AI to accurately extract meaningful information. Therefore, the course emphasizes the importance of obtaining clean data.

Lecture 1: Introduction

Introduction to the course. Why do we need it? What is an EEG from a Brain-Computer interface point of view?

Lecture 2: Is it EEG

How to confirm that the collected data is a clean EEG that can be used for future AI feature extraction

Lecture 3: Before EEG measurement

What is the difference between Active and Passive Electrodes,  Wet and Dry Electrodes, and what to choose?

Lecture 4: Start Measure EEG

Recommendations on what needs to be done to minimize noise during the recording of EEG data

Lecture 5: Dataset

Where to find the right EEG dataset, and the main gap for EEG datasets 

Lecture 6: How BCI hardware works

How BCI converts microvolt data to a digital format and details about the ADS1299 analog-to-digital converter

Lecture 7. Introduction to Brain-Computer Interface with PiEEG

How to read data with the PiEEG brain-computer interface. Measure EEG with RaspberryPI

Lecture 8. Introduction to Brain-Computer Interface with ardEEG and ironbci

How to read data with the ardEEG and ironbci brain-computer interfaces. Measure EEG with Arduino and STM32

Lecture 9. How to measure EMG and EOG with a Brain-Computer Interface

Details how to measure EMG and EOG with Brain-Computer Interfaces. Locations for Electrodes. 

Lecture 10. Improve the result and Conclusion

Future steps