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


A team of researchers associated with the Applied Machine Learning Laboratory 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 Leads: Leslie Collins, Boyla Mainsah

Project Manager: Kaleb Kassaw



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Manchester Metropolitan University Computer Science