The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects

By Benjamin Blankertz, Florian Losch, Matthias Krauledat, Guido Dornhege, Gabriel Curio, K. Müller. IEEE Transactions on Biomedical Engineering, 2008

339 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/deb5b5b7cbc6e542b4334854be9efcd9d360b784

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Optimizing Spatial filters for Robust EEG Single-Trial Analysis (2008, background): “The perirolandic sensorimotor cortices show rhythmic macroscopic EEG oscillations ( µ - rhythm, sensori motor rhythm, SMR) ([24], [20]), with spectral peak energies of about 8–14 Hz (localized predominantly over the postcentral somatosensory cortex) and around 20 Hz (over”
Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, background): “However, how to perform optimal channel selection is not a trivial task, since selecting channels manually based on neurophysiologic knowledge does not necessarily yield optimal results compared to using all the EEG channels [6].”
Towards a Cure for BCI Illiteracy (2009, methodology): “Machine Learning based BCIs use EEG features of larger complexity that can be ﬁtted better to the individual characteristics of brain patterns of each user (see Blankertz et al. 2007, 2008b; Dornhege et al. 2004, 2007; Mu¨ller et al. 2003, 2008).”
Toward a hybrid brain–computer interface based on imagined movement and visual attention (2010, methodology): “Improved signal processing can also make BCIs work for most but not all users [21].”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, methodology): “…the use of modern machine learning and signal processing methods, that allowed to relocate the burden of training from a learning subject toward statistical learning machines and thereby achieve BCI communication for a naïve user already in the first session (Blankertz et al., 2002, 2007a, 2008b).”
Could Anyone Use a BCI? (2010, background): “Improved signal processing merits further study, and will probably continue to reduce but not eliminate “BCI illiteracy” ( Blankertz et al. 2008; Brunner et al. 2010).”
EEG-Based Brain-Controlled Mobile Robots: A Survey (2013, background): “However, the BCI systems that are based on a single signal do not work for all users [90], [111]–[113].”
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b (2012, methodology): “For effective use of the CSP algorithm, several parameters have to be speciﬁed, namely, the frequency for band-pass ﬁltering of the EEG measurements, the time interval of the EEG measurements taken relative to the stimuli, and the subset of CSP ﬁlters to be used (Blankertz et al., 2008b).”
Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces (2009, neutral): “Detailed information about this technique can be found in Blankertz et al. (2008b).”
Neurophysiological predictor of SMR-based BCI performance (2010, neutral): “Details on the heuristics underlying the selection of frequency band and time interval can be found in Blankertz et al. (2008b).”
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, background): “[31] B.”
Brain Computer Interfaces, a Review (2012, methodology): “Well-known BCI systems such as Wadsworth [146], Berlin [147], or Graz [148] BCIs employ sensorimotor rhythms as control signals.”