Data-Driven NeuroTechnology lab
Data-Driven NeuroTechnology lab
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Pieter-Jan Kindermans
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Rethinking BCI paradigm and machine learning algorithm as a symbiosis: zero calibration, guaranteed convergence and high decoding performance
Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]
Unsupervised learning for brain–computer interfaces based on event-related potentials
Improving zero-training brain-computer interfaces by mixing model estimators
Improving learning from label proportions by reducing the feature dimensionality
Learning from Label Proportions in BCI -- a Symbiotic Design for Stimulus Preservation and Signal Decoding
Learning from label proportions in brain-computer interfaces: online unsupervised learning with guarantees
Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees
Mixing two unsupervised estimators for event-related potential decoding: An online evaluation
Workshops of the Sixth International Brain--Computer Interface Meeting: brain--computer interfaces past, present, and future
Improving our understanding of transfer-learning in ERP based BCI
True Zero-Training Brain-Computer Interfacing -- An Online Study
Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller
Transferring Unsupervised Adaptive Classifiers Between Users of a Spatial Auditory Brain-Computer Interface
Zero Training for BCI -- Reality for BCI Systems Based on Event-Related Potentials
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