The Berlin brain-computer interface: EEG-based communication without subject training

By Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, K. Müller, V. Kunzmann, Florian Losch, Gabriel Curio. IEEE transactions on neural systems and rehabilitation engineering, 2006

358 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/fc230790fcfdcb7c3d311b3dfcbc656bda9ff9ae

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, methodology): “Other recent BBCI lines of research comprise (a) mental typewriter experiments (Müller and Blankertz, 2006; Blankertz et al., 2006b), (b) the online use of the error potential, (c) investigation of synchronization features that capture dynamic interactions of brain areas (Meinecke et al., 2005;…”
Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design (2013, methodology): “With this approach the training time before the user can control the BCI is much shorter (about 20 min for 2 classes), see, e.g., (Millán et al., 2002; Blankertz et al., 2006).”
Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. (2008, positive): “Recently, so-called mathematical programming methods like the Linear Programming Machines (LPMs) (Blankertz et al., 2006c; Bennett and Mangasarian, 1992; Vapnik, 1995; Müller et al., 2001) have been proposed which fulfill this condition.”
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces (2011, methodology): “Each cell of the matrix contains a character: [A-Z], [1] [2] [3] [4] [5] [6] [7] [8] [9] , and [_].”
Silent speech interfaces (2010, background): “Examples of some current BCIs include the ‘‘Thought Translation Device” (Birbaumer, 2000) and the ‘‘Berlin Brain Computer Interface” (Blankertz et al., 2006).”
Covariate Shift Adaptation by Importance Weighted Cross Validation (2007, methodology): “The experimental setting is described in more detail in the references (Blankertz et al., 2007, 2006; Sugiyama et al., 2006).”
Turning Shortcomings into Challenges: Brain-Computer Interfaces for Games (2009, background): “With machine learning, the system analyzes the user’s brain activity and adjusts to that [39].”
Brain–computer interface systems: progress and prospects (2007, methodology): “Some subjects have achieved simple control with minimal training using µ and β rhythms associated with imagery of specific movements [42,43] . µ BCI systems have also been used to control devices, such as an orthosis or neuroprosthesis [44–47] .”