Classifying mental workload using AI with a brain computer interface device at Insai

Posted 3 weeks ago

Industry MSc Thesis that allows you to work with real EEG data, build AI models and gain valuable experience working in a startup environment. 

 

Company background

Insai is a medical device startup based in Copenhagen. Our company is working on  developing better diagnostics and treatments for neuropsychiatric disorders. Insai is building a medical device that can monitor brain activity and deliver targeted neuromodulation to restore healthy neural function. The Nexus headband enables take-home, tailored, and remotely supervised brain therapy that can scale to patients in need.  

Project 

The objective of this project is to classify mental workload using AI with a wearable brain computer interface. The project involves conducting a pilot study to collect data from healthy subjects as they perform a series of cognitive tasks with varying levels of difficulty. The task will then be to build a classifier to distinguish EEG data based on the cognitive workload. This will involve exploring the state of the art signal processing and classification methods. The project will involve all of the steps required from data collection to data analysis and result in determining the most performant model.

This project presents the prospective student with an opportunity to work with real EEG data, build AI models and gain valuable experience working in a startup environment. Throughout the project, the student will work with a range of brain-computer interface devices, ranging from consumer-focused devices to research-grade devices. 

Project requirements

  • MSc student

  • Experience with EEG and/or biometric signal processing.

  • Experience with Python

  • Experience with Machine Learning / Deep Learning

Project start: Spring Semester 2023

Responsible institution/department: Insai ApS, DTU Health

DTU supervisor: Sadasivan Puthusserypady,

Contact information: Kaleem Corbin,  

Allowed no of students per report (1-4): Up to 2 students

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