Using EEG and Azure Machine Learning to Perform Lie Detection

Overview

Explore EEG-based lie detection using Azure Machine Learning. Learn about data collection, model building, and result analysis, with insights on limitations and future directions.

Syllabus

• Introduction to EEG and Lie Detection
Overview of EEG technology and its applications
Basics of lie detection and its scientific background
• Data Collection for EEG-based Lie Detection
Setting up EEG equipment and software
Understanding brain signals and data acquisition techniques
Ethical considerations and data privacy
• Preprocessing EEG Data
Cleaning raw EEG data
Feature extraction methods
Dimensionality reduction techniques
• Introduction to Azure Machine Learning
Overview of Azure ML services and tools
Setting up an Azure ML workspace
Importing and managing datasets
• Building Machine Learning Models
Selecting appropriate ML algorithms for EEG data
Model training and validation with Azure ML
Hyperparameter tuning and optimization
• Analyzing Model Results
Evaluating model performance
Understanding false positives and false negatives in lie detection
Visualizing results and insights
• Limitations of EEG-based Lie Detection
Technical and ethical challenges
Reliability and validity of the models
• Future Directions in Lie Detection with AI
Emerging trends in AI and EEG
Potential impacts and advancements in the field
• Hands-On Project
Designing a simple lie detection experiment
Collecting and analyzing EEG data using Azure ML
Presenting findings and discussing implications
• Conclusion and Course Summary
Key takeaways and best practices
Further reading and resources for continued learning

Subjects

Conference Talks