Combined Optimization of Spatial and Temporal Filters for Improving Brain-Computer Interfacing

By Guido Dornhege, Benjamin Blankertz, Matthias Krauledat, Florian Losch, Gabriel Curio, K. Müller. IEEE Transactions on Biomedical Engineering, 2006

406 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/221a870d8c51b29b5951e2071d0a0b3b7c3741ae

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In Cognitive Neuroscience

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Citations in the corpus

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, background): “Common sparse spatio-spectral patterns (CSSSP) (Dornhege et al., 2006) are a comparatively more advanced procedure where the common spectral patterns across channels are investigated.”
The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, methodology): “Recently efficient extensions of CSP for multiclass settings (Dornhege et al., 2004a) as well as optimized spatio-temporal filter extensions of CSP have been proposed (Lemm et al., 2005; Dornhege et al., 2006; Tomioka et al., 2006, in press).”
Brain Computer Interfaces, a Review (2012, methodology): “For these reasons, several methods improving the original CSP method have been proposed to increase the performance: Wavelet Common Spatial Pattern (WCSP) [185], Common Spatio-Spectral Pattern (CSSP) [186], and Common Sparse Spectral Spatial Pattern (CSSSP) [187].”
Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. (2008, positive): “A very successful method to determine spatio-temporal filters for a BCI system is the so-called Common Spatial Pattern (CSP) algorithm, which has recently been extended and improved in various directions (Lemm et al., 2005; Dornhege et al., 2007b, 2006; Tomioka et al., 2006, 2007).”
Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces (2015, background): “, [75], [76], [77], [78], [79], [80], [81], 2) outlier/artifact removal in small sample size settings, as when few data are available, a single outlier (artefacts being unfortunately rather common in EEG measurements [82]) can have dramatic negative effects on the quality of the optimized BCI, 3) co”
EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges (2019, background): “Common sparse spatio-spectral patterns (CSSSP) [90] is a more reﬁned technique which aims to ﬁnd spectral patterns which are common across channels, as opposed to the individual spectral patterns in each channel.”
BCILAB: a platform for brain–computer interface development (2013, result): “Spec-CSP has been shown [22] to be roughly comparable in performance to other adaptive state-of-the-art frequency-learning methods such as CSSP [58] and CSSSP [59], which are therefore not discussed here.”
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008, methodology): “It improves the CSSP algorithm by performing simultaneous optimization of an arbitrary (Finite Impulse Response) FIR filter within the CSP algorithm [7].”
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b (2012, methodology): “…Pattern (CSSSP) which performs simultaneous optimization of an arbitrary Finite Impulse Response (FIR) ﬁlter within the CSP algorithm (Dornhege et al., 2006); and the SPEC-trally weighted Common Spatial Pattern (SPEC-CSP) algorithm algorithm by performing autonomous selection of…”
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, methodology): “the popular filter bank CSP (FBCSP) method [7] and others [61, 88, 161]).”
A New Discriminative Common Spatial Pattern Method for Motor Imagery Brain–Computer Interfaces (2009, background): “In the literature, common sparse spectral spatial pattern (CSSSP) [4], subband CSP (SBCSP) [5], Filter bank Manuscript received April 14, 2009; revised June 9, 2009.”
Introduction to machine learning for brain imaging (2011, methodology): “For example, the common spatial pattern (CSP) (cf. Fukunaga, 1972; Koles, 1991; Ramoser et al., 2000) algorithm and derivatives thereof Blankertz et al. (2008); Lemm et al. (2005); Dornhege et al. (2006); Tomioka and Müller (2010) are widely used to extract discriminative oscillatory features.”