The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects

By Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, K. Müller, Gabriel Curio. NeuroImage, 2007

1,006 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/f78993603048819634758ff2f039689749112987

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Neurophysiological predictor of SMR-based BCI performance (2010, methodology): “Details on the heuristics underlying the selection of frequency band and time interval can be found in Blankertz et al. (2008b). Note that also subject-optimized spatial filters were determined by”
Brain-Computer Interfaces: A Gentle Introduction (2009, methodology): “BCIs have been validated with many applications, including spelling devices, simple computer games, environmental control, navigation in virtual reality, and generic cursor control applications [26, 33, 34].”
Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces (2011, methodology): “The best class combination of tasks is chosen depending on the best classification performance and used for the feedback session, see [23], [ 25 ], and [26] for more details on this procedure.”
Brain-computer interfacing using modulations of alpha activity induced by covert shifts of attention (2011, background): “Alternatively, one may rely on other paradigms such as motor imagery [7,8].”
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b (2012, background): “INTRODUCTION The challenge in Motor Imagery-based BCI (MI-BCI), which translates the mental imagination of movement to commands, is the huge inter-subject variability with respect to the characteristics of the brain signals (Blankertz et al., 2007).”
Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach (2018, background): “The first dataset2 (Dataset 1 [5]) was recorded from seven healthy subjects.”
Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces (2009, neutral): “…BCI systems is to minimize the time required before accurate BCI performance is possible (subject training Elbert et al. (1980); Rockstroh et al. (1984); Birbaumer et al. (2000) or gathering calibration data in oﬀ-line runs to feed machine learning (ML) algorithms Blankertz et al. (2007, 2008a)).”
Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges (2010, background): “A second level of self-adaptation concerns the choice of the EEG phenomena that each user better controls, which can range from evoked potentials like P300 [46, 138] or SSVEP [182, 58, 22] to spontaneous signals like slow cortical potentials [12] and rhythmic activity [6, 208, 150, 120, 15].”
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, methodology): “To date, a whole range of methods have been proposed that fulfill this task, such as heuristics [54], filter-banks [25,55], Bayesian methods [26], among many others.”
Subject-Independent Brain–Computer Interfaces Based on Deep Convolutional Neural Networks (2019, methodology): “However, unfortunately, the process of calibration is an inconvenient and time-consuming task, which requires approximately 20–30 min for a reliable decoder to be built [24], [25].”
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).”
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008, methodology): “COMMON SPATIAL PATTERN ALGORITHM The neurophysiological background of motor-imagery based BCIs is that motor activity, both actual and imagined [9],[10], causes an attenuation or increase of localized neural rhythmic activity called Event-Related Desynchronization or Event-Related Synchronization (E”
Optimizing Spatial filters for Robust EEG Single-Trial Analysis (2008, methodology): “FOR SUBJECT AU NO REASONABLE CLASSIFIER COULD BE TRAINED (CROSS-VALIDATION ACCURACY BELOW 65% IN THE CALIBRATION DATA), SEE [2] FOR AN ANALYSIS OF THAT SPECIFIC CASE.”