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

What you'll learn

Understanding Machine Learning for EEG feature extraction

Python Programming for Machine Learning : Learners will receive scripts in Python for machine learning tasks

ML for EEG Data: Learners will acquire the skills to make feature extraction from EEG data

Applying Advanced Machine Learning Methods: Learners will be able to apply advanced ML methods with scikit-learn

Requirements

Knowledge of working with Python, Numpy, Pandas, Scipy etc

Gmail

Knowledge of signal processing for neuroscience

Knowledge of Machine Learning

Knowledge about neuroscience

Description

Lecture 1: IntroductionHere 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 ColabThis 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 InterfaceThis chapter covers the essential hardware used in EEG-based brain-computer interfaces. Lecture 4: Data EvaluationWe 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 DatasetLearn 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 EEGThis 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.Lecture 7: Hyperparameter TuningImproving your model’s performance requires fine-tuning. This chapter covers strategies for hyperparameter optimization using grid search, ensuring you get the most accurate predictions from your EEG-based models.Lecture 8: Conclusion, Future Steps, and CollaborationIn the final chapter, we wrap up the course and discuss possible next steps. and opportunities to collaborate with the broader BCI and neuroscience community.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Lecture 2. Connect to Google Colab

Lecture 2 Connect to Google Colab

Section 3: Lecture 3. Hardware for Brain Computer Interface

Lecture 3 Hardware for Brain Computer Interface

Section 4: Lecture 4. Data Evaluation

Lecture 4 Data Evaluation

Section 5: Lecture 5. Prepare dataset

Lecture 5 Prepare Dataset

Section 6: Lecture 6. Machine Learning for stress detection via EEG

Lecture 6 Lecture 6. Machine Learning for stress detection via EEG

Section 7: Lecture 7. Hyperparameter tuning

Lecture 7 Hyperparameter tuning

Section 8: Lecture 8. Conclusion, Future steps and Collaboration

Lecture 8 Conclusion, Future steps and Collaboration

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.,Data Scientists and Machine Learning Practitioners: Those who are interested in applying data science and machine learning techniques to biosignals, with a specific focus on EEG data.,Biomedical Engineers and Technologists: Individuals working in the biomedical field who need to process and analyze EEG data as part of their work in developing medical devices or diagnostics.,Neuroscientists and Researchers: Professionals and academics who want to leverage Python for analyzing EEG data to advance their research in neuroscience and related fields.