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Random Forests for Feature Selection in Non-invasive Brain-Computer Interfacing.

, , and . CHI-KDD, volume 7947 of Lecture Notes in Computer Science, page 207-216. Springer, (2013)

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Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis., , , , , , , , , and 38 other author(s). NeuroImage, (2021)Brain-computer interface adaptation for an end user to compete in the Cybathlon., , and . SMC, page 1803-1808. IEEE, (2016)Adaptive hybrid brain-computer interaction: Ask a trainer for assistance!, , and . EMBC, page 1493-1496. IEEE, (2014)Random Forests for Feature Selection in Non-invasive Brain-Computer Interfacing., , and . CHI-KDD, volume 7947 of Lecture Notes in Computer Science, page 207-216. Springer, (2013)Bring mental activity into action! An enhanced online co-adaptive brain-computer interface training protocol., , , , , and . EMBC, page 2323-2326. IEEE, (2015)Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis, , , , , , , , , and 38 other author(s). NeuroImage, (August 2021)A co-adaptive sensory motor rhythms Brain-Computer Interface based on common spatial patterns and Random Forest., , , , and . EMBC, page 1049-1052. IEEE, (2015)Reduction of EEG artifacts in simultaneous EEG-fMRI: Reference layer adaptive filtering (RLAF)., , , , and . EMBC, page 3803-3806. IEEE, (2015)