Free Download Chatgpt & Brain-Computer Interfaces: Vibe Coding For All
Published 8/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 791.36 MB | Duration: 1h 18m
Without Programming Skills Guide how we can connect our brain with AI (ChatGPT) via Brain-computer interface
What you'll learn
Understanding how to Vibe Coding to ChatGPT create Machine learning algorithms for EEG feature extraction
Understanding how to use OpenAI to maje feature extraction from EEG data
Connecting Brain-Computer Interface to OpenAI and feature extraction in real-time
No programming experience need it, you will find all in the cruise
Requirements
Gmail
OpenAI account
OpenAI key
Knowledge about neuroscience
Description
Lecture 1: IntroductionIntroduction 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 InterfaceIntroduction to Hardware. How to measure EEG, Difference between real-time and non-real-time applications, etc Lecture 3: Dataset for the CourseWhere 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 EEGHow to create a machine learning model via Vibe coding to detect the Emotional stage via EEG signals Lecture 5: EEG detects emotions via OpenAIConnect OpenAI and start making feature extraction from EEG data directly in ChatGPT Lecture 6: Signal Processing and Feature Extraction with OpenAICreate 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 EEGConnect the ironbci Brain-computer interface to OpenAI. Send data from BCI directly to OpenAI for feature extraction Lecture 8. How to improve the Result. ConclusionHow can we improve accuracy, future steps, and prospect direction for Large Language Model and EEG
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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