A hierarchical Bayesian approach for learning sparse spatio-temporal decompositions of multichannel EEG
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TLDR
A statistical modeling framework is introduced for learning spatio-temporal decompositions of multiple-trial EEG data recorded under two contrasting experimental conditions and a variational Bayesian (VB) algorithm is developed for statistical inference of the hierarchical model.About:
This article is published in NeuroImage.The article was published on 2011-06-15 and is currently open access. It has received 54 citations till now. The article focuses on the topics: Infomax & Statistical model.read more
Citations
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Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI
TL;DR: A novel algorithm, namely temporally constrained sparse group spatial pattern (TSGSP), is proposed for the simultaneous optimization of filter bands and time window within CSP to further boost classification accuracy of MI EEG.
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Sparse Bayesian Classification of EEG for Brain–Computer Interface
TL;DR: A sparse Bayesian method is introduced by exploiting Laplace priors, namely, SBLaplace, for EEG classification by learning a sparse discriminant vector with a Laplace prior in a hierarchical fashion under a Bayesian evidence framework.
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L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI
TL;DR: An L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further and improves the recognition accuracy which is significantly higher than that of the MCCA.
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Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface
TL;DR: The proposed SFBCSP is a potential method for improving the performance of MI-based BCI by optimizing the spatial patterns and gives overall better MI classification accuracy in comparison with several competing methods.
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Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces
TL;DR: The experimental results confirm that superiority of the proposed MKELM-based method for accurate classification of EEG associated with motor imagery in BCI applications is confirmed.
References
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Maximum likelihood from incomplete data via the EM algorithm
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EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
Arnaud Delorme,Scott Makeig +1 more
TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.