FAMOX (JUNIOR RESEARCH GROUP)

ADAPTIVE NEURONAL SIGNAL PROCESSING METHODS FOR THE FACILITATION OF ATTEMPTED MOTOR EXECUTION


Relevant for Research Area

B - Core Technologies

C - Applications


Summary

FAMOX aims at developing multivariate machine learning methods, which can serve to decode predictive information about an upcoming motor task from pre-trial brain signals. The use of such data analysis methods is exemplified in a BCI-supported closed-loop scenario, where a brain-state decoding technique is utilized in the context of a hand motor training. This closed-loop training shall prospectively be applied with patients, that have experienced motor deficits by stroke. Besides the decoding task, important other practical data analysis problems are tackled in FAMOX, which are relevant to closed-loop applications of neurotechnological systems. Approaches to reduce the calibration and ramp-up time for brain signal decoding shall be tackled with methods, that allow to learn how to transfer hyperparameters, features or decoding models between users and sessions. Furthermore we develop a framework for data driven characterization of task-relevant brain states with clustering methods. In the context of adaptive deep brain stimulation (DBS), we explore machine learning algorithms, which help to select stimulation parameters optimally for the currently observed brain state.


Research Status

We succeeded in identifying and decoding oscillatory subspace components from the EEG, whose band power is informative in order to predict the behavioral performance of a subject in the following trial [1]. Subspace components derived by SpoC were found capable to predict reaction time for both, healthy users and stroke patients, and we have investigated the relation between various performance metrics, which are relevant in clinical hand motor assessment.

[1 ] Meinel A, Casta├▒o-Candamil S, Reis J., Tangermann M (2016) Pre-Trial EEG-based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task. Frontiers in Human Neuroscience, volume: 10, issue: 170.