Udemy – ChatGPT & Brain-Computer Interfaces: Vibe Coding for all

• Lecture 1: Introduction: Introduction to the course. Why do we need it? Why Large Language Model and ChatGPT? Limitations of standart machine learning implementation and prospects for Large Language Model models?
• Lecture 2: Hardware for Brain-Computer Interface: Introduction to Hardware. How to measure EEG, Difference between real-time and non-real-time applications, etc
• Lecture 3: Dataset for the Course: Where to find a dataset for EEG research. How to choose a dataset, etc. Introduction to the dataset.
• Lecture 4: Machine Learning with Vibe Coding to detect emotions via EEG: How to create a machine learning model via Vibe coding to detect the Emotional stage via EEG signals
• Lecture 5: EEG detects emotions via OpenAI: Connect OpenAI and start making feature extraction from EEG data directly in ChatGPT 
• Lecture 6: Signal Processing and Feature Extraction with OpenAI: Create signal processing via vibe coding and continue to make feature extraction via OpenAI
• Lecture 7. Brain-computer interface connects to OpenAI to make feature extraction from EEG: Connect the ironbci Brain-computer interface to OpenAI. Send data from BCI directly to OpenAI for feature extraction
• Lecture 8. How to improve the Result. Conclusion: How can we improve accuracy, future steps, and prospect direction for Large Language Model and EEG

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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