Exploring novel machine learning techniques for Brain Computer Interface (BCI) applications

Project Summary

A team of researchers associated with the Applied Machine Learning Lab in Duke’s ECE department will lead a team of students in developing novel machine learning techniques that will be used for improving brain computer interfaces (BCIs) using electroencephalography (EEG) data.  Students will learn how to pre-process EEG data, extract EEG features, and train machine learning algorithms for character selection in a spelling interface that allows “locked in” individuals, like Stephen Hawking, to communicate with the outside world.  In addition to developing machine learning algorithms, students will work to develop a dashboard to visualize EEG signals, trained classifier parameters, classifier outputs, and spelling decisions made by the BCI.

 

Project Lead: Leslie Collins

Project Manager: Evan Stump

Themes and Categories
Year
2022
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

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