| File Name: | Machine Learning (Python) For Neuroscience Practical Course |
| Content Source: | https://www.udemy.com/course/machine-learning-python-for-neuroscience-practical-course/?couponCode=LETSLEARNNOW |
| Genre / Category: | Other Tutorials |
| File Size : | 567 MB |
| Publisher: | udemy |
| Updated and Published: | June 19, 2025 |
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
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.
Lecture 7: Hyperparameter Tuning
Improving 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 Collaboration
In the final chapter, we wrap up the course and discuss possible next steps. and opportunities to collaborate with the broader BCI and neuroscience community.
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.
- 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.
DOWNLOAD LINK: Machine Learning (Python) For Neuroscience Practical Course
FILEAXA.COM – is our main file storage service. We host all files there. You can join the FILEAXA.COM premium service to access our all files without any limation and fast download speed.
