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Individualized hand motor training under suitable brain states to improve performance and learning after stroke


Project run time: 2018-2020


SuitAble is kindly supported by

  • The DFG (grant 387670982)
  • The bwHPC initiative (grant INST 39/963-1 FUGG)


In stroke rehabilitation training, hand motor tasks are repeatedly executed to support plastic changes. Even though executing the same task again and again, performance fluctuations between repeated executions can be observed. Measuring and analyzing brain activity (EEG signals), we found evidence that the observed performance variability can partially be explained and even temporally predicted by exploiting individual oscillatory sources of brain signals. They represent objective features which can explain a task-specific suitable brain state.
Current decoding algorithms, however, reveal unsolved challenges: as features need to be optimized individually, substantial training data is required. Furthermore, the achievable decoding quality varies over time due to non-stationarities and discovered brain signal features often lack clinical interpretability, which impedes cross-subject comparisons.

The project SuitAble addresses these challenges by investigating machine learning solutions for extracting informative oscillatory subspaces from EEG recordings. Furthermore, these algorithmic approaches are combined with transfer learning over multiple data sets. In a clinical proof-of-concept study, SuitAble utilizes the envisioned algorithmic advances to enable a novel brain state-dependent closed-loop hand motor training and  evaluate it with chronic stroke patients. The training extends an established protocol by equipping it with a novel single-trial strategy exploiting the ongoing brain state. Providing explicit feedback about the neural features within the training, we expect patients to learn how to actively adopt suitable brain states and thus accelerate their motor skill learning.

Within SuitAble, we will employ the novel brain state-dependent training approach: we investigate its feasibility for stroke patients, test its efficacy and compare its efficiency to a hand motor training disregarding brain states. Furthermore, dictionary-based brain state decoding will permit to describe clinically interpretable training-induced changes of functionally relevant oscillatory processes, e.g., in the sensori-motor and visual attention domain, and allow for comparison with healthy controls.


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Selected publications

Meinel A, Castaño-Candamil S, Reis J, Tangermann M, Pre-Trial EEG-based Single-Trial Motor Performance Prediction to Enhance Neuroergonomics for a Hand Force Task. Frontiers in Human Neuroscience, 10(170), 2016 [url]

Tangermann M, Reis J, Meinel A, Commonalities of Motor Performance Metrics are Revealed by Predictive Oscillatory EEG Components. Proceedings of the 3rd International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX 2015), p32-38, Lisbon, 2015

Castaño-Candamil S, Meinel A, Reis J, Tangermann M, P186. Correlates to influence user performance in a hand motor rehabilitation task. Clinical Neurophysiology, 126(8):e166 - e167, 2015 [url]

Meinel A, Castaño-Candamil JS, Dähne S, Reis J, Tangermann M, EEG Band Power Predicts Single-Trial Reaction Time in a Hand Motor Task. Proc. Int. IEEE Conf. on Neural Eng. (NER), IEEE, p182-185, 2015