| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Advanced Foundations of Neural Signal Processing | |||
| 1. 1 1 The Mathematics of Neural Signals.mp4 | 214.7 MB | ||
| 2 - Frequency Domain & Time Frequency Decomposition | |||
| 3 - Spatial Filtering & Advanced Feature Engineering | |||
| 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 | ||
| 4 - Advanced EEG EMG Modeling with Machine Learning & AI | |||
| 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 MB | ||
| 16. Hands on Lab 4.html | 7.6 KB | ||
| 5 - Practical Tools MNE + BrainFlow + Python Ecosystem | |||
| 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 | ||
| 6 - Advanced BCI System Engineering | |||
| 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 | ||
| 7 - Ethics Reliability & Research Level Design | |||
| 25. 7 1 Experimental Design for High Quality Neural Data.mp4 | 265.1 MB | ||
| 9. 3 1 Common Spatial Patterns (CSP).mp4 | 196.3 MB | ||
| 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 MB | ||
| 8. Hands on Lab 2.html | 4.2 KB | ||
| 2. 1 2 Noise Artefact Modeling & Removal.mp4 | 253 MB | ||
| 3. 1 3 Understanding Cognitive and Motor Rhythms (µ β γ).mp4 | 203.4 MB | ||
| 4. Hands on Lab.html | 3.6 KB |
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
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