In this regard, we propose a Bayesian framework to simultaneously optimize a number of filter banks and spatial filters according to the patterns of brain ...
The ability to discriminate and classify different tasks is a crucial requirement for any Electroencephalogram (EEG) based Brain com- puter Interface (BCI).
A Bayesian Framework to Optimize Double Band Spectra Spatial Filters for Motor Imagery Classification ... optimize a number of filter banks and spatial filters ...
In this paper, we propose a novel Bayesian frame-work for discriminative feature extraction for motor imagery classification in an EEG-based BCI, in which ...
Feb 8, 2015 · There are two challenging problems in classifying a single-trial EEG of motor imagery. One is spectral filter optimization - The frequency bands ...
Nov 30, 2015 · This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns.
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A novel Bayesian framework for discriminative feature extraction in ...
pubmed.ncbi.nlm.nih.gov › ...
In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI.
Missing: Double Spectra
In this paper, a general framework is proposed for simultaneous design of spatial and spectral filters, which are used to extract discriminant features from ...
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Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims.
In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI, in which the ...