Machine Learning (Python) for Neuroscience Practical course
Specially applied course for Machine Learning with Python for Neuroscience, short way to start use EEG in life
Lecture 1: Introduction
Here you will find a short introduction to the course. We outline the objectives, structure, and practical outcomes. This sets the stage for hands-on experience in machine learning with EEG signals.
Lecture 2: Connect to Google Colab
This chapter provides a step-by-step guide on how to connect to and work in Google Colab. You’ll learn how to set up your environment, install required libraries, and ensure you are ready to run the code examples provided throughout the course.
Lecture 3: Hardware for Brain-Computer Interface
This chapter covers the essential hardware used in EEG-based brain-computer interfaces.
Lecture 4: Data Evaluation
We dive into evaluating the quality of your EEG data. This chapter explores techniques to inspect, clean, and annotate EEG recordings, ensuring that your data is reliable before moving forward with analysis or machine learning tasks.
Lecture 5: Prepare the Dataset
Learn how to transform raw EEG signals into structured datasets suitable for machine learning. This chapter includes labeling, segmenting, and feature extraction techniques—critical steps for successful model training and testing.
Lecture 6: Machine Learning for Stress Detection via EEG
This is the core of the course. You’ll learn how to apply machine learning algorithms to classify stress states from EEG data. This includes model selection, training pipelines, and evaluation metrics using libraries such as Scikit-learn and TensorFlow.
