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Make Data Work
December 1–3, 2015 • Singapore

Using EEG and machine learning for lie detection

Jennifer Marsman (Microsoft)
4:50pm–5:30pm Wednesday, 12/02/2015
Data Science and Advanced Analytics
Location: 321-322 Level: Intermediate
Average rating: ****.
(4.50, 6 ratings)
Slides:   1-ZIP 

Prerequisite Knowledge

A basic understanding of machine learning will help (like understanding that past data is needed to build a model to generate predictions, data is split into training and test set, etc.). I don't assume any knowledge of Azure.

Description

Today, we have the technology to “read minds” (well, EEG waves!). Using an EPOC headset from Emotiv, I can capture the big data stream of EEG from our brains. In this session, I will share my results on a “lie detector” experiment comparing brain waves when telling the truth and lying. I have built classifiers based on the EEG data using Azure Machine Learning to predict whether a subject is telling the truth. The effectiveness of multiple classifiers can be easily compared. This session will be a fun look inside your brain waves along with demonstrations of data processing and predictive analytics. Attendees will gain exposure to the Emotiv EPOC headset and Azure Machine Learning.

Agenda:

  • What is EEG?
  • Very quick intro to Emotiv EPOC headset
  • Project Lie Detection – goals, tools used, experiment procedure
  • Explain P300 ERP research
  • Quick intro to Azure Machine Learning
  • Show machine learning model, data cleaning process, and results
  • Limitations of approach, summary, and next steps/resources

In a 40-minute session, I won’t have time to demonstrate the cool things that the EPOC headset can do, like detection of facial expressions, detection of emotional state, and training a machine learning model to employ mental commands (making a virtual object move via thought). But I can certainly bring the headset and show curious people during a break.

Photo of Jennifer Marsman

Jennifer Marsman

Microsoft

Jennifer Marsman is the principal software engineer for Microsoft’s AI for Earth Group, where she uses data science, machine learning, and artificial intelligence to aid with clean water, agriculture, biodiversity, and climate change. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. Previously, Jennifer was a software developer in Microsoft’s Natural Interactive Services Division, where she authored two patents related to search and data mining algorithms. She has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Since 2016, Jennifer has been recognized as one of the top 100 most influential individuals in artificial intelligence and machine learning by Onalytica, reaching the #2 slot in 2018, and in 2009 was chosen as the “techie whose innovation will have the biggest impact” by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the Best in Role award for technical evangelism, Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity and Inclusion Award, Gold Club, and Platinum Club. Jennifer is a frequent speaker at software development conferences around the world. She holds a bachelor’s degree in computer engineering and a master’s degree in computer science and engineering from the University of Michigan in Ann Arbor, where she specialized in artificial intelligence and computational theory. To learn more, check out her blog.