@article {4598, title = {Multidimensional feature analysis shows stratification in robotic-motor-training gains based on the level of pre-training motor impairment in stroke.}, journal = {Annu Int Conf IEEE Eng Med Biol Soc}, volume = {2024}, year = {2024}, month = {2024 Jul}, pages = {1-5}, abstract = {

Stroke involves heterogeneity in injury and ongoing endogenous recovery, which are seldom stratified before testing post-stroke robot assisted motor training (RAMT). Pretraining variations, especially sensory-motor differences may also affect the gains achieved from the RAMT. Moreover, one assessment test may not effectively characterize the baseline sensory-motor status or the RAMT gains. Pre-therapy stratification may help personalize therapy and increase therapy gains. Towards this goal, we propose a data-driven approach to assess multiple functional scores with t-distributed stochastic neighbor embedding and affinity propagation clustering, both for pre-therapy and RAMT gains. Data included behavioral scores from 27 people with chronic stroke who underwent RAMT for finger movement. Three clusters were observed at start-of-therapy (SoT), concurrent with the overall impairment level. Four clusters were observed for the RAMT gains, indicating specific improvements. The SoT clusters showed agreement with the RAMT gain clusters, suggesting that the pre-therapy state, assessed across multiple domains, could be useful in guiding RAMT interventions to improve outcomes.

}, keywords = {Aged, Cluster Analysis, Female, Humans, Male, Middle Aged, Robotics, Stroke, Stroke Rehabilitation}, issn = {2694-0604}, doi = {10.1109/EMBC53108.2024.10781784}, author = {Rueda-Parra, Sebastian and Perry, Joel C and Wolbrecht, Eric T and Reinkensmeyer, David J and Gupta, Disha} } @article {2143, title = {Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {183-6}, abstract = {

We summarize results from a series of related studies that aim to develop a motor-imagery-basedbrain-computer interface\ using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.

}, keywords = {Algorithms, Artificial Intelligence, Cluster Analysis, Computer User Training, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Middle Aged, Paralysis, Pattern Recognition, Automated, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875548}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792289}, author = {Jeremy Jeremy Hill and Lal, T.N and Schr{\"o}der, Michael and Hinterberger, T. and Wilhelm, Barbara and Nijboer, F and Mochty, Ursula and Widman, Guido and Elger, Christian and Sch{\"o}lkopf, B and K{\"u}bler, A. and Niels Birbaumer} }