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

What is it EEG from a brain-computer interface point of view, and how to receive clean EEG data during measurement

What you'll learn
- How to measure EEG. What is it EEG from Brain-Computer interface point of view
- Which difference for dry and wet electrodes
- How receive clean EEG data and reduce noise
- How measure EEG with RaspberryPi and Arduino

Requirements
- Knowledge about neuroscience

Description
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

Who this course is for:
- Individuals with a strong interest in EEG and brain-computer interfaces who want to explore the technical aspects of EEG signal processing as a hobby or personal project.
- Graduate and advanced undergraduate students in fields such as neuroscience, biomedical engineering, data science, and psychology, as well as educators looking to integrate EEG signal processing into their curriculum.
- Neuroscientists and Researchers: Professionals and academics who want to leverage Python for analyzing EEG data to advance their research in neuroscience and related fields.
- For neuro enthusiasts
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