Analysis of Graded Sensorimotor Rhythms for Brain-Computer Interface Applications

The primary aim of this dissertation is to investigate whether graded effort associated with a movement task can be distinguished from scalp EEG recordings. This endeavor is motivated by the need for numerous, natural, and intuitive command signals in brain-controlled assistive devices, such as BCIs, to achieve fine control. Measurable changes in brain activity during a graded cognitive task can serve as commands that bridge the gap between intent and fine control and thereby play a vital role in therapeutic protocols aimed at recovery of function.

A novel protocol was first established that guides subjects through a graded movement task while providing immediate visual feedback. First in a healthy cohort and then in individuals with chronic stroke, I tested the ability of the protocol to elicit and model levels of effort while the subject attempts the graded movement task. After a wide search into features of the electroencephalogram (EEG) that may highlight the different levels of effort, I arrived at an 8 – 30 Hz rhythm that was differentially attenuated according to the movement. Applying common modeling techniques led to the ability to predict these gradations from EEG with greater than chance accuracy. A forward-looking study concludes this dissertation in which the bridge between mental intent and feedback is closed with real-time prediction of effort from the EEG.

This work suggests a model that predicts graded movement in healthy and clinical populations is feasible. The ability to connect intent and execution in those who may have lost function can play a vital role in therapeutic programs aimed at recovery of function and independence.

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Recommended citation: Haddix. (2021). “Analysis of Graded Sensorimotor Rhythms for Brain-Computer Interface Applications.” Theses and Dissertations–Biomedical Engineering. 74.