Neurophysiological predictor of SMR-based BCI performance

By Benjamin Blankertz, Claudia Sannelli, Sebastian Halder, Eva M. Hammer, Andrea Kübler, K. Müller, Gabriel Curio, Thorsten Dickhaus. NeuroImage, 2010

691 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d0007aa91ac3f78db181fe185a39a79b41e45db9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Fabien Lotte
2. Cuntai Guan
3. K. Müller
4. Benjamin Blankertz
5. Carmen Vidaurre
6. Michael W. Tangermann
7. Luis F. Nicolás-Alonso
8. Jaime Gómez Gil

Citations in the corpus

• Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces (2011, methodology): “2) Dataset 2: This dataset was recorded jointly by the T¨ ubingen and the BBCI groups [ 24 ].”
• Riemannian Approaches in Brain-Computer Interfaces: A Review (2017, neutral): “Indeed, current BCIs too often recognize erroneous commands from the user, which results in rather low accuracy and information transfer rate, even in laboratory conditions [7], [8].”
• EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, positive): “Further neuroscientific studies on brain connectivity [30,31], neuroimaging [19], and mental workload estimation [32,33], among others, could be conducted based on our dataset.”
• EEG datasets for motor imagery brain–computer interface (2017, positive): “Predicting a subject’s performance by using the resting state or the background noise from EEG are some examples of this [3–5].”
• Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design (2013, background): “…users’ characteristics, such as psychosocial and physiological parameters (e.g., gender, instrument playing, fine motor skills) or brain structures, can predict control performances for Mu-rhythm based BCI (Blankertz et al., 2010; Halder et al., 2011, 2013; Hammer et al., 2012; Randolph, 2012).”
• Brain Computer Interfaces, a Review (2012, methodology): “Sensorimotor rhythms have been used to control BCIs, because people can learn to generate these modulations voluntarily in the sensorimotor rhythms [36,142].”
• Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019, background): “For example, one can use the resting state of the brain signal to predict whether this subject is an excellent BCI user [63].”
• Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (2011, methodology): “Acknowledgements The authors thank Martijn Schreuder for his help with recording and preparing the auditory ERP data set, Claudia Sannelli for her help with recording and preparing the RT EEG data set, the authors of [33] for providing the motor imagery data set.”
• Robust artifactual independent component classification for BCI practitioners (2014, methodology): “During the three online runs of 100 trials each participant controlled an application which provided continuous visual feedback in the form of a horizontally moving cursor [50].”
• The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, methodology): “A state-of-the-art classifier (Blankertz et al., 2010b), borrowed from BCI technology, was trained to discriminate deviant vs. standard ERPs.”