Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI).

By Christoph Guger, Herbert Ramoser, Gert Pfurtscheller. IEEE transactions on rehabilitation engineering, 2000

408 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d4514060d93d32ea0f308ab34839d2a4403ee802

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Towards adaptive classification for BCI (2006, background): “These phenomena are well-studied and consistently reproducible features in EEG recordings and are used as the basis of a number of BCI systems (e.g. [8] [13]).”
Neurophysiological predictor of SMR-based BCI performance (2010, neutral): “Other approaches are able to provide BCI control already in the very first session, but still need a calibration period of about 30 minutes (Guger et al. (2000); Blankertz et al. (2008a, 2007)).”
The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, methodology): “…(spatial filters that are optimized for the discrimination of different condition, cf. Common spatial pattern (CSP) analysis) for the use in BCI systems (Ramoser et al., 2000) and reported in (Guger et al., 2000a) results from a feedback study with a CSP-based BCI operating on a 27 channel EEG.”
Optimizing Spatial filters for Robust EEG Single-Trial Analysis (2008, methodology): “to the specific characteristics of each user ([19], [2], [7]).”
A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces (2009, methodology): “One of the most effective algorithms for MI-BCI is based on common spatial pattern (CSP) technique [1], [3].”
The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects (2008, background): “is to calibrate the system to the specific charateristics of each user [1], [27].”
Current trends in Graz Brain-Computer Interface (BCI) research. (2000, methodology): “continuous feedback is not possible, it appears that instantaneous feedback information improves the left–right differentiation of EEG patterns [6], [18], [19], [21].”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, positive): “The spectral [86] and spatial features [81], [87] of SMR have been extensively studied, and can be used for classifying the states of motor imageries, e.”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, background): “…al 2005, Xu et al 2004a, Wang et al 2004c, Li et al 2004a) Common spatial patterns (CSP) (Blanchard and Blankertz 2004, Dornhege et al 2003, 2004, Guger et al 2000b, Krauledat et al 2004, Muller et al 2003b, Pfurtscheller et al 2000, Pfurtscheller and Neuper 2001, Ramoser et al 2000, Townsend et…”
Seperability of four-class motor imagery data using independent components analysis (2006, methodology): “The usefulness of the CSP method has already been proven in a number of BCI studies (Guger et al 2000, Ramoser et al 2000, Lemm et al 2005).”
Deep learning for healthcare applications based on physiological signals: A review (2018, background): “The realtime nature of this application makes human signal interpretation impossible [78].”
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, positive): “Our dataset could, therefore, support a broad range of BCI research such as subject-dependent or independent BCI [15–17], session-to-session transfer [18], and prediction of a user’s BCI performance [19–21], among others [22].”
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].”