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Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?, , , , , , , , , and 29 other author(s). Human Brain Mapping, 41 (14): 3839-3854 (July 2020)Multimodal Head-Mounted Virtual-Reality Brain-Computer Interface for Stroke Rehabilitation - A Clinical Case Study with REINVENT., , , , , and . HCI (9), volume 11574 of Lecture Notes in Computer Science, page 165-179. Springer, (2019)Embodiment Is Related to Better Performance on a Brain-Computer Interface in Immersive Virtual Reality: A Pilot Study., , , , , , , , and . Sensors, 20 (4): 1204 (2020)The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data., , , , , , , , , and 7 other author(s). Frontiers Neuroinformatics, (2019)Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis., , , , , , , , , and 38 other author(s). NeuroImage, (2021)Pipeline for Analyzing Lesions After Stroke (PALS)., , , , and . Frontiers Neuroinformatics, (2018)REINVENT: A low-cost, virtual reality brain-computer interface for severe stroke upper limb motor recovery., , , and . VR, page 385-386. IEEE Computer Society, (2017)Electromyography as a Suitable Input for Virtual Reality-Based Biofeedback in Stroke Rehabilitation., , , , , and . HCI (33), volume 1032 of Communications in Computer and Information Science, page 274-281. Springer, (2019)Visual processing of actions directed towards three-dimensional objects in immersive virtual reality may involve holistic processing of object shape., , and . Frontiers Virtual Real., (2022)Transfer of a skilled motor learning task between virtual and conventional environments., , , and . VR, page 401-402. IEEE Computer Society, (2017)