404 research outputs found
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
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
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
When dealing with electro or magnetoencephalography records, many supervised
prediction tasks are solved by working with covariance matrices to summarize
the signals. Learning with these matrices requires using Riemanian geometry to
account for their structure. In this paper, we propose a new method to deal
with distributions of covariance matrices and demonstrate its computational
efficiency on M/EEG multivariate time series. More specifically, we define a
Sliced-Wasserstein distance between measures of symmetric positive definite
matrices that comes with strong theoretical guarantees. Then, we take advantage
of its properties and kernel methods to apply this distance to brain-age
prediction from MEG data and compare it to state-of-the-art algorithms based on
Riemannian geometry. Finally, we show that it is an efficient surrogate to the
Wasserstein distance in domain adaptation for Brain Computer Interface
applications
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
In applied fields where the speed of inference and model flexibility are
crucial, the use of Bayesian inference for models with a stochastic process as
their prior, e.g. Gaussian processes (GPs) is ubiquitous. Recent literature has
demonstrated that the computational bottleneck caused by GP priors or their
finite realizations can be encoded using deep generative models such as
variational autoencoders (VAEs), and the learned generators can then be used
instead of the original priors during Markov chain Monte Carlo (MCMC) inference
in a drop-in manner. While this approach enables fast and highly efficient
inference, it loses information about the stochastic process hyperparameters,
and, as a consequence, makes inference over hyperparameters impossible and the
learned priors indistinct. We propose to resolve the aforementioned issue and
disentangle the learned priors by conditioning the VAE on stochastic process
hyperparameters. This way, the hyperparameters are encoded alongside GP
realisations and can be explicitly estimated at the inference stage. We believe
that the new method, termed PriorCVAE, will be a useful tool among approximate
inference approaches and has the potential to have a large impact on spatial
and spatiotemporal inference in crucial real-life applications. Code showcasing
the PriorCVAE technique can be accessed via the following link:
https://github.com/elizavetasemenova/PriorCVA
Challenge IEEE-ISBI/TCB : Application of Covariance matrices and wavelet marginals
This short memo aims at explaining our approach for the challenge IEEE-ISBI
on Bone Texture Characterization. In this work, we focus on the use of
covariance matrices and wavelet marginals in an SVM classifier.Comment: 9 pages, 4 Figues, 2 Tables, Challenge IEEE-ISBI : Bone Texture
Characterizatio
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
We introduce a novel kernel that models input-dependent couplings across
multiple latent processes. The pairwise joint kernel measures covariance along
inputs and across different latent signals in a mutually-dependent fashion. A
latent correlation Gaussian process (LCGP) model combines these non-stationary
latent components into multiple outputs by an input-dependent mixing matrix.
Probit classification and support for multiple observation sets are derived by
Variational Bayesian inference. Results on several datasets indicate that the
LCGP model can recover the correlations between latent signals while
simultaneously achieving state-of-the-art performance. We highlight the latent
covariances with an EEG classification dataset where latent brain processes and
their couplings simultaneously emerge from the model.Comment: 17 pages, 6 figures; accepted to ACML 201
Riemannian approaches in Brain-Computer Interfaces: a review
International audienceAlthough promising from numerous applications, current Brain-Computer Interfaces (BCIs) still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and the non-stationarity of ElectroEncephaloGraphic (EEG) signals, they require long calibration times and are not reliable. Thus, new approaches and tools, notably at the EEG signal processing and classification level, are necessary to address these limitations. Riemannian approaches, spearheaded by the use of covariance matrices, are such a very promising tool slowly adopted by a growing number of researchers. This article, after a quick introduction to Riemannian geometry and a presentation of the BCI-relevant manifolds, reviews how these approaches have been used for EEG-based BCI, in particular for feature representation and learning, classifier design and calibration time reduction. Finally, relevant challenges and promising research directions for EEG signal classification in BCIs are identified, such as feature tracking on manifold or multi-task learning
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