| Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke C Tozlu, D Edwards, A Boes, D Labar, KZ Tsagaris, J Silverstein, ... Neurorehabilitation and neural repair 34 (5), 428-439, 2020 | 55 | 2020 |
| A framework for combining rTMS with behavioral therapy KZ Tsagaris, DR Labar, DJ Edwards Frontiers in Systems Neuroscience 10, 82, 2016 | 38 | 2016 |
| Using data from the Microsoft Kinect 2 to quantify upper limb behavior: a feasibility study B Dehbandi, A Barachant, D Harary, JD Long, KZ Tsagaris, SJ Bumanlag, ... IEEE journal of biomedical and health informatics 21 (5), 1386-1392, 2016 | 29 | 2016 |
| Paired associative stimulation as a tool to assess plasticity enhancers in chronic stroke J Silverstein, M Cortes, KZ Tsagaris, A Climent, LM Gerber, C Oromendia, ... Frontiers in Neuroscience 13, 792, 2019 | 13 | 2019 |
| Predicting response to motor therapy in chronic stroke patients using Machine Learning C Tozlu, D Edwards, A Boes, D Labar, KZ Tsagaris, J Silverstein, HP Lane, ... bioRxiv, 457416, 2018 | 3 | 2018 |
| NeuroMeasure: a software package for quantification of cortical motor maps using frameless stereotaxic transcranial magnetic stimulation MB Gerber, AC McLean, SJ Stephen, AG Chalco, UM Arshad, ... Frontiers in Neuroinformatics 13, 23, 2019 | 2 | 2019 |
| Transcranial Magnetic Stimulation (TMS) as a tool for stroke motor recovery: What are we targeting?(P5. 164) M Gunduz, K Nearing, D Labar, T Zoe, G Thickbroom, M Cortes, M Fink, ... Neurology 84 (14_supplement), P5. 164, 2015 | | 2015 |
| Predicting response to motor therapy in chronic stroke patients based on clinical and connectivity measurements using Machine Learning C Tozlu, D Edwards, A Boes, KZ Tsagaris, J Silverstein, HP Lane, ... | | |
