347 research outputs found
BCI Signal Classification using a Riemannian-based kernel
International audienceThe use of spatial covariance matrix as feature is investigated for motor imagery EEG-based classification. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms significantly state of the art results without the need for spatial filtering
Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features
Accurate, fast, and reliable multiclass classification of
electroencephalography (EEG) signals is a challenging task towards the
development of motor imagery brain-computer interface (MI-BCI) systems. We
propose enhancements to different feature extractors, along with a support
vector machine (SVM) classifier, to simultaneously improve classification
accuracy and execution time during training and testing. We focus on the
well-known common spatial pattern (CSP) and Riemannian covariance methods, and
significantly extend these two feature extractors to multiscale temporal and
spectral cases. The multiscale CSP features achieve 73.7015.90% (mean
standard deviation across 9 subjects) classification accuracy that surpasses
the state-of-the-art method [1], 70.614.70%, on the 4-class BCI
competition IV-2a dataset. The Riemannian covariance features outperform the
CSP by achieving 74.2715.5% accuracy and executing 9x faster in training
and 4x faster in testing. Using more temporal windows for Riemannian features
results in 75.4712.8% accuracy with 1.6x faster testing than CSP.Comment: Published as a conference paper at the IEEE European Signal
Processing Conference (EUSIPCO), 201
EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features
Riemannian geometry has been successfully used in many brain-computer
interface (BCI) classification problems and demonstrated superior performance.
In this paper, for the first time, it is applied to BCI regression problems, an
important category of BCI applications. More specifically, we propose a new
feature extraction approach for Electroencephalogram (EEG) based BCI regression
problems: a spatial filter is first used to increase the signal quality of the
EEG trials and also to reduce the dimensionality of the covariance matrices,
and then Riemannian tangent space features are extracted. We validate the
performance of the proposed approach in reaction time estimation from EEG
signals measured in a large-scale sustained-attention psychomotor vigilance
task, and show that compared with the traditional powerband features, the
tangent space features can reduce the root mean square estimation error by
4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.Comment: arXiv admin note: text overlap with arXiv:1702.0291
Classification of covariance matrices using a Riemannian-based kernel for BCI applications
International audienceThe use of spatial covariance matrix as a feature is investigated for motor imagery EEG-based classification in Brain-Computer Interface applications. A new kernel is derived by establishing a connection with the Riemannian geometry of symmetric positive definite matrices. Different kernels are tested, in combination with support vector machines, on a past BCI competition dataset. We demonstrate that this new approach outperforms significantly state of the art results, effectively replacing the traditional spatial filtering approach
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