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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Konstantin Sudarikov
ECoG Processing for Motion Kinematics Reconstruction
Data Science
(Master’s programme)
2019
Nowadays various neural interfaces are currently being developed in order to facilitate interaction between a human and a computer. Bidirectional neural interfaces are especially interesting because they allow transferring both information from person to computer and in the opposite direction. In particular, such an interface could allow a person facing problems with movement to process signals sent by the brain and send back with a predicted action (which can be transmitted, for example, with a robotic arm). In this work, using electrocorticography data of the patient and data with movements of his fingers, we solved the problem of predicting movements of a person’s fingers according to the activity of his brain. The results of the application of machine learning algorithms showed that with an accuracy of more than 75% and a relatively low error, one can predict human movement based on the ECoG results.

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