473 research outputs found
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
Tensor Decomposition for EEG Signal Retrieval
Prior studies have proposed methods to recover multi-channel
electroencephalography (EEG) signal ensembles from their partially sampled
entries. These methods depend on spatial scenarios, yet few approaches aiming
to a temporal reconstruction with lower loss. The goal of this study is to
retrieve the temporal EEG signals independently which was overlooked in data
pre-processing. We considered EEG signals are impinging on tensor-based
approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this
study, we collected EEG signals during a resting-state task. Then, we defined
that the source signals are original EEG signals and the generated tensor is
perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources
are separated using a basic non-negative CPD and the relative errors on the
estimates of the factor matrices. Comparing the similarities between the source
signals and their recovered versions, the results showed significantly high
correlation over 95%. Our findings reveal the possibility of recoverable
temporal signals in EEG applications
Reliable Green Routing Using Two Disjoint Paths
Network robustness and throughput can be improved by routing each demand d via two disjoint paths (2DP). However, 2DP routing increases energy usage while providing lower linkutilization and redundancy. In this paper, we address an NP-complete problem, called 2DP-EAR, that aims to switch off redundant nodes and links while guaranteeing two constraints:traffic demands must be afforded 2DP, and maximum link utilization. We design an efficient heuristic, called 2DP by Nodes First (2DP-NF). We have extensively evaluated the performance of 2DP-NF on both real and/or synthetic topologies and traffic demands. As compared to using Shortest Path routing, on the GÉANT network, 2DP-NF can save around 20% energy by switching off links only with negligible effects on path delays and link utilization, even for MLU below 30%. Furthermore, 2DP-NF can obtain 39.7% power savings by switching off both nodes and links on the GÉANT network
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