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Name
Affiliation
Country
Title
Benjamin Blankertz
TU Berlin
Germany
The virtue of combining a model-based and a data-driven approach for Brain-Computer Interfacing.
Guan Cuntai
Nanyang University of Technology
Singapore
Brain-Computer Interface-based Gait Prediction for Lower-Limb Stroke Rehabilitation
Pamela Douglas
UCLA
USA
Transcranial Ultrasound for testing Brain Computational Models
Martin Hebart
Max Planck Institute for Human Cognitive and Brain Sciences
Revealing interpretable object representations from human visual cortex and artificial neural networks
Andrea Kübler
University of Würzburg
Correlation between neurophysiological measures of consciousness and BCI performance in a locked-in patient
Klaus-Robert Müller
Interpreting Deep Learning Models for Multi-modal Neuroimaging
Marieke Mur
Western University
Canada
Visual representation learning in humans and deep neural networks
Srikantan Nagarajan
University of California, San Francisco
Advances in Imaging of Neural Oscillations
Daniel B. Rubin
Harvard Medical School
BrainGate: An Intracortical Brain-Computer Interface for the Restoration of Communication and Functional Independence for People with Paralysis