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ChatGPT & Brain-Computer Interfaces: Vibe Coding for all
Rating: 4.5 out of 5(29 ratings)
589 students

ChatGPT & Brain-Computer Interfaces: Vibe Coding for all

Without Programming Skills Guide how we can connect our brain with AI (ChatGPT) via Brain-computer interface
Created byDr. Ildar Rakhmatulin
Last updated 8/2025
English
English [Auto],

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

Course content

8 sections • 8 lectures • 1h 17m total length

  • Introduction8:27

    Introduction to the course. Why do we need it? Why ChatGPT? Prospects and Limitations of LLM?

Requirements

  • Gmail
  • OpenAI account
  • OpenAI key
  • Knowledge about neuroscience
  • For the last chapter - Brain-Computer Interface as ironbci etc

Description

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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