ℓ1-penalized linear mixed-effects models for high dimensional data with application to BCI
Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ1-penalized linear regression mixed-effects model for a large scale real worl...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 56; no. 4; pp. 2100 - 2108 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
15.06.2011
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8119 1095-9572 |
| DOI | 10.1016/j.neuroimage.2011.03.061 |
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| Abstract | Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ1-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.
► We apply a novel mixed-effects model to high dimensional BCI data for the first time. ► The model inherently compensates shifts in the input space. ► We can now distinguish within- and between-subject variability. ► The model leads to a more compact and superior BCI subject-independent classifier. ► The framework is applicable to a wide range of experiments in many domains. |
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| AbstractList | Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ1-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.
► We apply a novel mixed-effects model to high dimensional BCI data for the first time. ► The model inherently compensates shifts in the input space. ► We can now distinguish within- and between-subject variability. ► The model leads to a more compact and superior BCI subject-independent classifier. ► The framework is applicable to a wide range of experiments in many domains. |
| Author | Danóczy, Márton Fazli, Siamac Müller, Klaus-Robert Schelldorfer, Jürg |
| Author_xml | – sequence: 1 givenname: Siamac surname: Fazli fullname: Fazli, Siamac email: fazli@cs.tu-berlin.de organization: Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany – sequence: 2 givenname: Márton surname: Danóczy fullname: Danóczy, Márton organization: Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany – sequence: 3 givenname: Jürg surname: Schelldorfer fullname: Schelldorfer, Jürg organization: ETH Zürich,Rämistrasse 101, 8092 Zürich, Switzerland – sequence: 4 givenname: Klaus-Robert surname: Müller fullname: Müller, Klaus-Robert organization: Berlin Institute of Technology, Franklinstr. 28/29, 10587 Berlin, Germany |
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| Cites_doi | 10.1016/S0013-4694(98)00084-4 10.1109/TNSRE.2003.814456 10.1016/j.neunet.2009.06.003 10.1016/j.neuroimage.2007.01.051 10.1109/MSP.2008.4408447 10.1109/TNSRE.2003.814484 10.1088/1741-2560/3/1/R02 10.1109/TBME.2006.883649 10.1016/j.neuroimage.2009.07.045 10.1111/j.1541-0420.2010.01391.x 10.1016/j.neuroimage.2005.05.032 10.1111/j.2517-6161.1996.tb02080.x 10.1371/journal.pone.0002967 10.1109/MSP.2008.4408441 10.1109/TNSRE.2006.875576 10.1016/j.neuroimage.2010.03.022 10.1109/TNSRE.2006.875557 10.1109/TBME.2002.803536 10.1111/j.1467-9868.2007.00627.x 10.1109/72.914517 10.1109/TBME.2009.2026181 10.1111/j.1467-9868.2005.00532.x 10.1146/annurev.bb.02.060173.001105 10.1109/86.895946 |
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| Keywords | Mixed-effects model BCI Sparsity Subject-independent |
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