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Found 17 papers, 7 papers with code
A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems in Wireless Sensor Networks
A wireless sensor network often relies on a fusion center to process the data collected by each of its sensing nodes.
Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses
In this context, generalized canonical correlation analysis (GCCA) is often used as a group analysis technique, which allows the extraction of correlated signal components from the neural activity of multiple subjects attending to the same stimulus.
Minimally Informed Linear Discriminant Analysis: training an LDA model with unlabelled data
Linear Discriminant Analysis (LDA) is one of the oldest and most popular linear methods for supervised classification problems.
Distributed Adaptive Signal Fusion for Fractional Programs
The distributed adaptive signal fusion (DASF) framework allows to solve spatial filtering optimization problems in a distributed and adaptive fashion over a bandwidth-constrained wireless sensor network.
A distributed neural network architecture for dynamic sensor selection with application to bandwidth-constrained body-sensor networks
We then show how we can use this dynamic selection to increase the lifetime of a wireless sensor network (WSN) by imposing constraints on how often each node is allowed to transmit.
A Distributed Adaptive Algorithm for Node-Specific Signal Fusion Problems in Wireless Sensor Networks
The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve these signal fusion problems in a distributed fashion, which reduces the communication and energy costs in the network.
A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems
Computing the optimal solution to a spatial filtering problems in a Wireless Sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used.
Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses
We show the superiority of the proposed stimulus-informed GCCA method based on the inter-subject correlation between electroencephalography responses of a group of subjects listening to the same speech stimulus, especially for lower amounts of data or smaller groups of subjects.
Bandwidth-efficient distributed neural network architectures with application to body sensor networks
In this paper, we describe a conceptual design methodology to design distributed neural network architectures that can perform efficient inference within sensor networks with communication bandwidth constraints.
Avoiding Post-Processing with Event-Based Detection in Biomedical Signals
Methods: We propose an event-based modeling framework that directly works with events as learning targets, stepping away from ad-hoc post-processing schemes to turn model outputs into events.
A Unified Algorithmic Framework for Distributed Adaptive Signal and Feature Fusion Problems -- Part II: Convergence Properties
This paper studies the convergence conditions and properties of the distributed adaptive signal fusion (DASF) algorithm, the framework itself having been introduced in a `Part I' companion paper.
A Unified Algorithmic Framework for Distributed Adaptive Signal and Feature Fusion Problems -- Part I: Algorithm Derivation
The proposed distributed adaptive signal fusion (DASF) algorithm is an iterative method that solves these types of problems by allowing each node to share a linearly compressed version of the local sensor signal observations with its neighbors to reduce the energy and bandwidth requirements of the network.
Grouped Variable Selection for Generalized Eigenvalue Problems
This group-sparsity allows, for instance, to perform sensor selection for spatio-temporal (instead of purely spatial) filters, and to select variables based on multiple generalized eigenvectors instead of only the dominant one.
End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax
Many electroencephalography (EEG) applications rely on channel selection methods to remove the least informative channels, e. g., to reduce the amount of electrodes to be mounted, to decrease the computational load, or to reduce overfitting effects and improve performance.
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation
Detectable change points include abrupt changes in the slope, mean, variance, autocorrelation function and frequency spectrum.
EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices
People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously.
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses
OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy, two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest.
