Optimal spatial filtering of single trial EEG during imagined hand movement.

By Herbert Ramoser, J. Müller-Gerking, Gert Pfurtscheller. IEEE transactions on rehabilitation engineering, 2000

2,522 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d1f7b2b520cd8ab7632591faf1fec028638c2519

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

In Electrical and Electronic Engineering

Citations in the corpus

Brain Computer Interfaces, a Review (2012, background): “It aims to make the subsequent classification much more effective, by designing a spatial filter that transforms the input data into output data with an optimal variance for the subsequent discrimination [183].”
Review of the BCI Competition IV (2012, positive): “A good example of such a lesson that can be learned from the competitions is that common spatial pattern analysis (CSP/CSSD; Koles, 1991; Ramoser et al., 2000; Blankertz et al., 2008b) and its variants are a robust tool for exploiting ERD/ERS effects (Pfurtscheller and da Silva, 1999): Almost all da”
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.”
Introduction to machine learning for brain imaging (2011, methodology): “As a result a number of novel data driven approaches speci ﬁ c to neuroscience have emerged: (a) dimension reduction and projection methods (e.g. Hyvärinen et al., 2001; Ziehe et al., 2000; Parra and Sajda, 2003; Morup et al., 2008; von Bünau et al., 2009; Blanchard et al., 2006; Bießmann et al., 20”
The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, methodology): “analysis) for the use in BCI systems (Ramoser et al., 2000) and reported in (Guger et al.”
Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). (2000, methodology): “7% was achieved for three other subjects in off-line analysis [14].”
A novel deep learning approach for classification of EEG motor imagery signals (2017, methodology): “Common spatial patterns (CSP) [6, 9] is a popular method among MI studies for extracting features.”
Current trends in Graz Brain-Computer Interface (BCI) research. (2000, methodology): “For this purpose, the CSP method was used to estimate spatial filters that reflect the specific activation of cortical areas during hand movement imagination [15].”
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, methodology): “Both types of features benefit from being extracted after spatial filtering [22, 136, 185, 188].”
Recipes for the linear analysis of EEG (2005, methodology): “The recovered components have been termed common spatial patterns (Koles, 1997; Ramoser et al., 2000).”
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms (2011, methodology): “mizing the discriminability of two classes [3], [4].”
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface (2008, methodology): “The objective of spatial filtering employing the CSP algorithm [12] in BCI is to compute features whose variances are optimal for discriminating two classes of EEG measurements [12],[13].”
Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting (2010, methodology): “a covariance matrix S(c,m) and the average spatial covariance matrix is then calculated for each class as [5]”
EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing (2011, methodology): “…containing imagined movements of either [26, 27] (i) Linear discriminant Analysis (LDA) [28] (ii) Resampling (ii) Common Spatial Patterns (CSP) [29] (ii) Quadratic discriminant analysis (QDA) [30] (iii) Artifact rejection (spike detection, bad window detection, bad channel detection, local…”
Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition (2020, methodology): “Most classical methods to tackle the session-to-session difference were based on the common spatial pattern (CSP) [17], which assumed the existence of a set of linear ﬁlters that could work across sessions.”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, methodology): “…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 al 2004, Xu et al 2004b) Principal component analysis (PCA) (Chapin et al 1999, Guan et al 2004, Hu et al 2004,…”
The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects (2008, methodology): “The spatial filters are calculated individually for each sub ject from the data of a calibration measurement by Common Spatial Pattern (CSP) analysis ([28,30,31]).”