Advanced EEG & ECG Analysis: Neural Networks, Chaos Theory, and Practical Coding Insights

In this video, I dive into the intricate world of EEG and ECG biosignals, examining how time series analysis can be enhanced using fuzzy logic and neural networks. I explore cutting-edge methods for detecting and predicting features in EEG signals, particularly in the context of epilepsy. Additionally, I discuss the fascinating intersection of biosignals and Chaos Theory, showcasing how these principles can aid in the early detection and prediction of seizures. I also utilize GPT-4 to efficiently summarize relevant research, and delve into practical coding examples to demonstrate the integration of these theories into tangible applications.

The tools I develop are available on https://bionichaos.com
You can support my work on   / bionichaos  

0:00:00 - Introduction to EEG and ECG Time Series Analysis
0:00:19 - EEG Data, Fuzzy Logic, and Neural Networks
0:00:43 - Exploring Chaos Theory in Biosignals and Utilizing GPT-4
0:01:19 - Focusing on EEG in Epilepsy and Seizure Prediction
0:02:02 - Investigating Leonov Exponents, Fractal Dimensions, and Seizure Dynamics
0:03:00 - Coding Aspects: Integrating JavaScript, HTML, and Chaos Theory
0:05:26 - Enhancing EEG Data Simulation with Realistic Models
0:07:26 - Implementing One-Over-F Noise and Chaotic Maps in EEG Simulation
0:10:03 - Visualizing EEG Data: Adding Interactive Features and Debugging
0:13:29 - Finalizing EEG Data Simulation and Visualization Techniques
0:17:01 - Specific Code Enhancements for Accurate EEG Simulation
0:20:08 - Advanced Implementation of Chaotic Maps and Pink Noise
0:22:09 - Concluding Remarks and Next Steps in EEG Data Analysis

#EEGAnalysis
#ECGTechnology
#TimeSeriesData
#NeuralNetworks
#FuzzyLogic
#ChaosTheory
#SeizurePrediction
#BiosignalProcessing
#GPT4
#EpilepsyResearch
#MachineLearning
#DataScience
#BiomedicalEngineering