Probing meaningfulness of oscillatory EEG components with bootstrapping, label noise and reduced training sets. - Sebastián Castaño-Candamil; Andreas Meinel; Sven Dähne; Michael Tangermann (EMBC, 2015)

EEG band power predicts single-trial reaction time in a hand motor task. - Andreas Meinel; Juan Sebastián Castaño-Candamil; Sven Dähne; Janine Reis; Michael Tangermann (NER, 2015)

Commonalities of Motor Performance Metrics are Revealed by Predictive Oscillatory EEG Components. - Michael Tangermann; Janine Reis; Andreas Meinel (NEUROTECHNIX, 2015)

Tikhonov Regularization enhances EEG-based Spatial filtering for single-Trial Regression. - Andreas Meinel; Fabien Lotte; Michael Tangermann (GBCIC, 2017)

Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods. - Sebastián Castaño-Candamil; Andreas Meinel; Michael Tangermann (CoRR, 2017)

Method to assess the functional role of noisy brain signals by mining envelope dynamics. - Andreas Meinel; Henrich Kolkhorst; Michael Tangermann (CoRR, 2018)

Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods. - Sebastián Castaño-Candamil; Andreas Meinel; Michael Tangermann (Frontiers Neuroinformatics, 2019)

Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems - Guidelines Derived from Simulation and Real-World Data. - Andreas Meinel; Sebastián Castaño-Candamil; Benjamin Blankertz; Fabien Lotte; Michael Tangermann (Neuroinformatics, 2019)

Machine learning methods for brain state-dependent motor rehabilitation after stroke - Andreas Meinel (2019)