Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation

Author:

Vidaurre C.,Ramos Murguialday A.,Haufe S.,Gómez M.,Müller K.-R.,Nikulin V.V.

Funder

German Ministry for Education and Research

Deutsche Forschungsgesellschaft

EU-FP7 MUNDUS

German Ministry for Education and Research as Berlin Big Data Centre

Berlin Center for Machine Learning

DFG

Institute for Information & Communications Technology Planning & Evaluation

Korea government

Spanish Ministry of Economy

HSE Basic Research Program

Russian Academic Excellence Project

Publisher

Elsevier BV

Subject

Cognitive Neuroscience,Neurology

Reference62 articles.

1. Large-scale assessment of a fully automatic co-adaptive motor imagery-based brain computer interface;Acqualagna;PLoS ONE, Public Library of Science,2016

2. Gamma band activity associated with bci performance: simultaneous meg/eeg study;Ahn;Front. Hum. Neurosci.,2013

3. High theta and low alpha powers may be indicative of bci-illiteracy in motor imagery;Ahn;PLoS One,2013

4. Facilitating motor imagery-based brain-computer interface for stroke patients using passive movement;Arvaneh;Neural Comput. Appl.,2017

5. The non-invasive berlin Brain-Computer interface: fast acquisition of effective performance in untrained subjects;Blankertz;Neuroimage,2007

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