Udemy - Neural Signal Processing and Applied AI
- Category Other
- Type Tutorials
- Language English
- Total size 4.1 GB
- Uploaded By freecoursewb
- Downloads 28
- Last checked 1 hour ago
- Date uploaded 13 hours ago
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Infohash : 6375BB57C28B8FC26AFD753B9F880A45DFC27D71
Neural Signal Processing & Applied AI
https://WebToolTip.com
Published 1/2026
Created by Data Science Academy
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 25 Lectures ( 4h 37m ) | Size: 3.88 GB
Learn to analyze neural signals using machine learning and deep learning techniques
What you'll learn
Understand and apply neural signal processing fundamentals, including time-domain, frequency-domain, and time-frequency analysis of EEG/EMG data.
Design robust preprocessing pipelines to clean neural signals using filtering, artifact removal, and covariance-based methods with professional tools like MNE-P
Extract advanced features from neural data, including CSP, bandpower, time-frequency features, and Riemannian geometry-based representations.
Build and evaluate machine learning models (LDA, SVM, ensemble methods) for neural signal classification and performance analysis.
Build complete end-to-end BCI systems, transforming neural signals into real-time commands for applications such as games, robotics, or interactive interfaces.
Requirements
Basic Python knowledge
Introductory understanding of machine learning (helpful, not mandatory)
Basic signal processing awareness (optional)
A computer capable of running Python
Curiosity and willingness to experiment
Files:
[ WebToolTip.com ] Udemy - Neural Signal Processing and Applied AI- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Advanced Foundations of Neural Signal Processing
- 1. 1 1 The Mathematics of Neural Signals.mp4 (214.7 MB)
- 2. 1 2 Noise Artefact Modeling & Removal.mp4 (253.0 MB)
- 3. 1 3 Understanding Cognitive and Motor Rhythms (ยต ฮฒ ฮณ).mp4 (203.4 MB)
- 4. Hands on Lab.html (3.6 KB)
- 5. 2 1 Spectral Analysis Techniques.mp4 (255.6 MB)
- 6. 2 2 Wavelet Transformations for EEG.mp4 (129.9 MB)
- 7. 2 3 Hilbert Huang Transform (HHT).mp4 (188.0 MB)
- 8. Hands on Lab 2.html (4.2 KB)
- 10. 3 2 Riemannian Geometry for EEG.mp4 (229.5 MB)
- 11. 3 3 Source Localization (Beginner Level).mp4 (318.6 MB)
- 12. Hands on Lab 3.html (7.3 KB)
- 9. 3 1 Common Spatial Patterns (CSP).mp4 (196.3 MB)
- 13. 4 1 Classical ML Approaches.mp4 (228.8 MB)
- 14. 4 2 Deep Learning for Neural Signals.mp4 (197.5 MB)
- 15. 4 3 Transformers for EEG EMG.mp4 (232.0 MB)
- 16. Hands on Lab 4.html (7.6 KB)
- 17. 5 1 MNE Python Advanced Workflows.mp4 (219.9 MB)
- 18. 5 2 BrainFlow for Real Time BCIs.mp4 (176.5 MB)
- 19. 5 3 Combining MNE + BrainFlow.mp4 (154.5 MB)
- 20. Hands on Lab 5.html (8.5 KB)
- 21. 6 1 Feature Pipelines for Real Time BCI.mp4 (254.8 MB)
- 22. 6 2 Calibration Free & Transfer Learning Approaches.mp4 (231.5 MB)
- 23. 6 3 Building End to End BCI Systems.mp4 (204.6 MB)
- 24. Hands on Lab 6.html (8.6 KB)
- 25. 7 1 Experimental Design for High Quality Neural Data.mp4 (265.1 MB)
- Bonus Resources.txt (0.1 KB)
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