Udemy – 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

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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