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

Partner: Udemy
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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: IntroductionIntroduction to the course. Why do we need it? What is an EEG from a Brain-Computer interface point of view? Lecture 2: Is it EEGHow to confirm that the collected data is a clean EEG that can be used for future AI feature extractionLecture 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 dataLecture 5: DatasetWhere to find the right EEG dataset, and the main gap for EEG datasets  Lecture 6: How BCI hardware worksHow BCI converts microvolt data to a digital format and details about the ADS1299 analog-to-digital converterLecture 7. Introduction to Brain-Computer Interface with PiEEGHow 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 InterfaceDetails how to measure EMG and EOG with Brain-Computer Interfaces. Locations for Electrodes.  Lecture 10. Improve the result and Conclusion Future steps
Category: Development > Data Science > Neuroscience
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Price: 34.99
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Source: Impact
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